From 4e8af9d8317f0422914f28d0e5effc652dc932ca Mon Sep 17 00:00:00 2001 From: Kevin Markham Date: Tue, 2 Mar 2021 08:53:12 -0500 Subject: [PATCH] update to scikit-learn 0.23.2 and Python 3.9.1 --- 01_machine_learning_intro.ipynb | 127 ++----------- 02_machine_learning_setup.ipynb | 121 ++----------- 03_getting_started_with_iris.ipynb | 144 ++++----------- 04_model_training.ipynb | 140 +++----------- 05_model_evaluation.ipynb | 170 +++++------------ 06_linear_regression.ipynb | 211 ++++++++-------------- 07_cross_validation.ipynb | 180 ++++++------------ 08_grid_search.ipynb | 181 +++++-------------- 09_classification_metrics.ipynb | 159 ++++------------ 10_categorical_features.ipynb | 281 +++++++++++++++-------------- README.md | 34 ++-- 11 files changed, 519 insertions(+), 1229 deletions(-) diff --git a/01_machine_learning_intro.ipynb b/01_machine_learning_intro.ipynb index f0c0a83..c6ee7a1 100644 --- a/01_machine_learning_intro.ipynb +++ b/01_machine_learning_intro.ipynb @@ -4,16 +4,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# What is machine learning, and how does it work? ([video #1](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1))\n", + "# What is Machine Learning, and how does it work? ([video #1](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1))\n", "\n", - "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos)." + "Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "![Machine learning](images/01_robot.png)" + "![Machine Learning](images/01_robot.png)" ] }, { @@ -22,19 +22,19 @@ "source": [ "## Agenda\n", "\n", - "- What is machine learning?\n", - "- What are the two main categories of machine learning?\n", - "- What are some examples of machine learning?\n", - "- How does machine learning \"work\"?" + "- What is Machine Learning?\n", + "- What are the two main categories of Machine Learning?\n", + "- What are some examples of Machine Learning?\n", + "- How does Machine Learning \"work\"?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## What is machine learning?\n", + "## What is Machine Learning?\n", "\n", - "One definition: \"Machine learning is the semi-automated extraction of knowledge from data\"\n", + "One definition: \"Machine Learning is the semi-automated extraction of knowledge from data\"\n", "\n", "- **Knowledge from data**: Starts with a question that might be answerable using data\n", "- **Automated extraction**: A computer provides the insight\n", @@ -45,7 +45,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## What are the two main categories of machine learning?\n", + "## What are the two main categories of Machine Learning?\n", "\n", "**Supervised learning**: Making predictions using data\n", " \n", @@ -81,14 +81,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## How does machine learning \"work\"?\n", + "## How does Machine Learning \"work\"?\n", "\n", "High-level steps of supervised learning:\n", "\n", - "1. First, train a **machine learning model** using **labeled data**\n", + "1. First, train a **Machine Learning model** using **labeled data**\n", "\n", " - \"Labeled data\" has been labeled with the outcome\n", - " - \"Machine learning model\" learns the relationship between the attributes of the data and its outcome\n", + " - \"Machine Learning model\" learns the relationship between the attributes of the data and its outcome\n", "\n", "2. Then, make **predictions** on **new data** for which the label is unknown" ] @@ -111,7 +111,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Questions about machine learning\n", + "## Questions about Machine Learning\n", "\n", "- How do I choose **which attributes** of my data to include in the model?\n", "- How do I choose **which model** to use?\n", @@ -126,7 +126,7 @@ "source": [ "## Resources\n", "\n", - "- Book: [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) (section 2.1, 14 pages)\n", + "- Book: [An Introduction to Statistical Learning](https://www.statlearning.com/) (section 2.1, 14 pages)\n", "- Video: [Learning Paradigms](http://work.caltech.edu/library/014.html) (13 minutes)" ] }, @@ -137,102 +137,9 @@ "## Comments or Questions?\n", "\n", "- Email: \n", - "- Website: http://dataschool.io\n", + "- Website: https://www.dataschool.io\n", "- Twitter: [@justmarkham](https://twitter.com/justmarkham)" ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ] } ], "metadata": { @@ -251,7 +158,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/02_machine_learning_setup.ipynb b/02_machine_learning_setup.ipynb index 2662428..291804c 100644 --- a/02_machine_learning_setup.ipynb +++ b/02_machine_learning_setup.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Setting up Python for machine learning: scikit-learn and Jupyter Notebook ([video #2](https://www.youtube.com/watch?v=IsXXlYVBt1M&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=2))\n", + "# Setting up Python for Machine Learning: scikit-learn and Jupyter Notebook ([video #2](https://www.youtube.com/watch?v=IsXXlYVBt1M&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=2))\n", "\n", - "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", + "Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", "\n", "**Note:** Since the video recording, the official name of the \"IPython Notebook\" was changed to \"Jupyter Notebook\". However, the functionality is the same." ] @@ -38,7 +38,7 @@ "\n", "### Benefits:\n", "\n", - "- **Consistent interface** to machine learning models\n", + "- **Consistent interface** to Machine Learning models\n", "- Provides many **tuning parameters** but with **sensible defaults**\n", "- Exceptional **documentation**\n", "- Rich set of functionality for **companion tasks**\n", @@ -46,14 +46,14 @@ "\n", "### Potential drawbacks:\n", "\n", - "- Harder (than R) to **get started with machine learning**\n", + "- Harder (than R) to **get started with Machine Learning**\n", "- Less emphasis (than R) on **model interpretability**\n", "\n", "### Further reading:\n", "\n", - "- Ben Lorica: [Six reasons why I recommend scikit-learn](http://radar.oreilly.com/2013/12/six-reasons-why-i-recommend-scikit-learn.html)\n", - "- scikit-learn authors: [API design for machine learning software](http://arxiv.org/pdf/1309.0238v1.pdf)\n", - "- Data School: [Should you teach Python or R for data science?](http://www.dataschool.io/python-or-r-for-data-science/)" + "- Ben Lorica: [Six reasons why I recommend scikit-learn](https://www.oreilly.com/content/six-reasons-why-i-recommend-scikit-learn/)\n", + "- scikit-learn authors: [API design for machine learning software](https://arxiv.org/pdf/1309.0238v1.pdf)\n", + "- Data School: [Should you teach Python or R for data science?](https://www.dataschool.io/python-or-r-for-data-science/)" ] }, { @@ -69,9 +69,9 @@ "source": [ "## Installing scikit-learn\n", "\n", - "**Option 1:** [Install scikit-learn library](http://scikit-learn.org/stable/install.html) and dependencies (NumPy and SciPy)\n", + "**Option 1:** [Install scikit-learn library](https://scikit-learn.org/stable/install.html) and dependencies (NumPy and SciPy)\n", "\n", - "**Option 2:** [Install Anaconda distribution](https://www.anaconda.com/download/) of Python, which includes:\n", + "**Option 2:** [Install Anaconda distribution](https://www.anaconda.com/products/individual) of Python, which includes:\n", "\n", "- Hundreds of useful packages (including scikit-learn)\n", "- IPython and Jupyter Notebook\n", @@ -124,9 +124,9 @@ "\n", "### IPython, Jupyter, and Markdown resources:\n", "\n", - "- [nbviewer](http://nbviewer.jupyter.org/): view notebooks online as static documents\n", - "- [IPython documentation](http://ipython.readthedocs.io/en/stable/)\n", - "- [Jupyter Notebook quickstart](http://jupyter.readthedocs.io/en/latest/content-quickstart.html)\n", + "- [nbviewer](https://nbviewer.jupyter.org/): view notebooks online as static documents\n", + "- [IPython documentation](https://ipython.readthedocs.io/en/stable/)\n", + "- [Jupyter Notebook quickstart](https://jupyter.readthedocs.io/en/latest/content-quickstart.html)\n", "- [GitHub's Mastering Markdown](https://guides.github.com/features/mastering-markdown/): short guide with lots of examples" ] }, @@ -149,102 +149,9 @@ "## Comments or Questions?\n", "\n", "- Email: \n", - "- Website: http://dataschool.io\n", + "- Website: https://www.dataschool.io\n", "- Twitter: [@justmarkham](https://twitter.com/justmarkham)" ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ] } ], "metadata": { @@ -263,7 +170,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/03_getting_started_with_iris.ipynb b/03_getting_started_with_iris.ipynb index 3c0a0ae..d6ec178 100644 --- a/03_getting_started_with_iris.ipynb +++ b/03_getting_started_with_iris.ipynb @@ -6,9 +6,9 @@ "source": [ "# Getting started in scikit-learn with the famous iris dataset ([video #3](https://www.youtube.com/watch?v=hd1W4CyPX58&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=3))\n", "\n", - "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", + "Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", "\n", - "**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)." + "**Note:** This notebook uses Python 3.9.1 and scikit-learn 0.23.2. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16." ] }, { @@ -17,9 +17,9 @@ "source": [ "## Agenda\n", "\n", - "- What is the famous iris dataset, and how does it relate to machine learning?\n", + "- What is the famous iris dataset, and how does it relate to Machine Learning?\n", "- How do we load the iris dataset into scikit-learn?\n", - "- How do we describe a dataset using machine learning terminology?\n", + "- How do we describe a dataset using Machine Learning terminology?\n", "- What are scikit-learn's four key requirements for working with data?" ] }, @@ -47,8 +47,19 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, + "outputs": [], + "source": [ + "# added empty cell so that the cell numbering matches the video" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { @@ -57,14 +68,14 @@ " \n", " " ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -74,17 +85,17 @@ ], "source": [ "from IPython.display import IFrame\n", - "IFrame('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', width=300, height=200)" + "IFrame('https://www.dataschool.io/files/iris.txt', width=300, height=200)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Machine learning on the iris dataset\n", + "## Machine Learning on the iris dataset\n", "\n", "- Framed as a **supervised learning** problem: Predict the species of an iris using the measurements\n", - "- Famous dataset for machine learning because prediction is **easy**\n", + "- Famous dataset for Machine Learning because prediction is **easy**\n", "- Learn more about the iris dataset: [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/datasets/Iris)" ] }, @@ -130,7 +141,9 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -170,10 +183,10 @@ " [5.4 3.4 1.5 0.4]\n", " [5.2 4.1 1.5 0.1]\n", " [5.5 4.2 1.4 0.2]\n", - " [4.9 3.1 1.5 0.1]\n", + " [4.9 3.1 1.5 0.2]\n", " [5. 3.2 1.2 0.2]\n", " [5.5 3.5 1.3 0.2]\n", - " [4.9 3.1 1.5 0.1]\n", + " [4.9 3.6 1.4 0.1]\n", " [4.4 3. 1.3 0.2]\n", " [5.1 3.4 1.5 0.2]\n", " [5. 3.5 1.3 0.3]\n", @@ -298,7 +311,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Machine learning terminology\n", + "## Machine Learning terminology\n", "\n", "- Each row is an **observation** (also known as: sample, example, instance, record)\n", "- Each column is a **feature** (also known as: predictor, attribute, independent variable, input, regressor, covariate)" @@ -378,7 +391,7 @@ "## Requirements for working with data in scikit-learn\n", "\n", "1. Features and response are **separate objects**\n", - "2. Features and response should be **numeric**\n", + "2. Features should always be **numeric**, and response should be **numeric** for regression problems\n", "3. Features and response should be **NumPy arrays**\n", "4. Features and response should have **specific shapes**" ] @@ -458,9 +471,9 @@ "source": [ "## Resources\n", "\n", - "- scikit-learn documentation: [Dataset loading utilities](http://scikit-learn.org/stable/datasets/)\n", + "- scikit-learn documentation: [Dataset loading utilities](https://scikit-learn.org/stable/datasets.html)\n", "- Jake VanderPlas: Fast Numerical Computing with NumPy ([slides](https://speakerdeck.com/jakevdp/losing-your-loops-fast-numerical-computing-with-numpy-pycon-2015), [video](https://www.youtube.com/watch?v=EEUXKG97YRw))\n", - "- Scott Shell: [An Introduction to NumPy](http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf) (PDF)" + "- Scott Shell: [An Introduction to NumPy](https://sites.engineering.ucsb.edu/~shell/che210d/numpy.pdf) (PDF)" ] }, { @@ -470,102 +483,9 @@ "## Comments or Questions?\n", "\n", "- Email: \n", - "- Website: http://dataschool.io\n", + "- Website: https://www.dataschool.io\n", "- Twitter: [@justmarkham](https://twitter.com/justmarkham)" ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ] } ], "metadata": { @@ -584,7 +504,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/04_model_training.ipynb b/04_model_training.ipynb index b98bc62..a677be2 100644 --- a/04_model_training.ipynb +++ b/04_model_training.ipynb @@ -4,11 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Training a machine learning model with scikit-learn ([video #4](https://www.youtube.com/watch?v=RlQuVL6-qe8&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=4))\n", + "# Training a Machine Learning model with scikit-learn ([video #4](https://www.youtube.com/watch?v=RlQuVL6-qe8&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=4))\n", "\n", - "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", + "Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", "\n", - "**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)." + "**Note:** This notebook uses Python 3.9.1 and scikit-learn 0.23.2. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16." ] }, { @@ -19,7 +19,7 @@ "\n", "- What is the **K-nearest neighbors** classification model?\n", "- What are the four steps for **model training and prediction** in scikit-learn?\n", - "- How can I apply this pattern to **other machine learning models**?" + "- How can I apply this pattern to **other Machine Learning models**?" ] }, { @@ -29,6 +29,15 @@ "## Reviewing the iris dataset" ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# added empty cell so that the cell numbering matches the video" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -41,14 +50,14 @@ " \n", " " ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -58,7 +67,7 @@ ], "source": [ "from IPython.display import IFrame\n", - "IFrame('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', width=300, height=200)" + "IFrame('https://www.dataschool.io/files/iris.txt', width=300, height=200)" ] }, { @@ -119,7 +128,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "*Image Credits: [Data3classes](http://commons.wikimedia.org/wiki/File:Data3classes.png#/media/File:Data3classes.png), [Map1NN](http://commons.wikimedia.org/wiki/File:Map1NN.png#/media/File:Map1NN.png), [Map5NN](http://commons.wikimedia.org/wiki/File:Map5NN.png#/media/File:Map5NN.png) by Agor153. Licensed under CC BY-SA 3.0*" + "*Image Credits: [Data3classes](https://commons.wikimedia.org/wiki/File:Data3classes.png#/media/File:Data3classes.png), [Map1NN](https://commons.wikimedia.org/wiki/File:Map1NN.png#/media/File:Map1NN.png), [Map5NN](https://commons.wikimedia.org/wiki/File:Map5NN.png#/media/File:Map5NN.png) by Agor153. Licensed under CC BY-SA 3.0*" ] }, { @@ -228,9 +237,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", - " metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n", - " weights='uniform')\n" + "KNeighborsClassifier(n_neighbors=1)\n" ] } ], @@ -256,9 +263,7 @@ { "data": { "text/plain": [ - "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", - " metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n", - " weights='uniform')" + "KNeighborsClassifier(n_neighbors=1)" ] }, "execution_count": 8, @@ -390,8 +395,8 @@ "# import the class\n", "from sklearn.linear_model import LogisticRegression\n", "\n", - "# instantiate the model (using the default parameters)\n", - "logreg = LogisticRegression()\n", + "# instantiate the model\n", + "logreg = LogisticRegression(solver='liblinear')\n", "\n", "# fit the model with data\n", "logreg.fit(X, y)\n", @@ -406,9 +411,9 @@ "source": [ "## Resources\n", "\n", - "- [Nearest Neighbors](http://scikit-learn.org/stable/modules/neighbors.html) (user guide), [KNeighborsClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) (class documentation)\n", - "- [Logistic Regression](http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression) (user guide), [LogisticRegression](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) (class documentation)\n", - "- [Videos from An Introduction to Statistical Learning](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)\n", + "- [Nearest Neighbors](https://scikit-learn.org/stable/modules/neighbors.html) (user guide), [KNeighborsClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) (class documentation)\n", + "- [Logistic Regression](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression) (user guide), [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) (class documentation)\n", + "- [Videos from An Introduction to Statistical Learning](https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/)\n", " - Classification Problems and K-Nearest Neighbors (Chapter 2)\n", " - Introduction to Classification (Chapter 4)\n", " - Logistic Regression and Maximum Likelihood (Chapter 4)" @@ -421,102 +426,9 @@ "## Comments or Questions?\n", "\n", "- Email: \n", - "- Website: http://dataschool.io\n", + "- Website: https://www.dataschool.io\n", "- Twitter: [@justmarkham](https://twitter.com/justmarkham)" ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ] } ], "metadata": { @@ -535,7 +447,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/05_model_evaluation.ipynb b/05_model_evaluation.ipynb index 5f910c0..7cc4a6e 100644 --- a/05_model_evaluation.ipynb +++ b/05_model_evaluation.ipynb @@ -4,11 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Comparing machine learning models in scikit-learn ([video #5](https://www.youtube.com/watch?v=0pP4EwWJgIU&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=5))\n", + "# Comparing Machine Learning models in scikit-learn ([video #5](https://www.youtube.com/watch?v=0pP4EwWJgIU&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=5))\n", "\n", - "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", + "Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", "\n", - "**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)." + "**Note:** This notebook uses Python 3.9.1 and scikit-learn 0.23.2. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16." ] }, { @@ -50,6 +50,15 @@ "2. Test the model on the **same dataset**, and evaluate how well we did by comparing the **predicted** response values with the **true** response values." ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# added empty cell so that the cell numbering matches the video" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -98,8 +107,8 @@ "# import the class\n", "from sklearn.linear_model import LogisticRegression\n", "\n", - "# instantiate the model (using the default parameters)\n", - "logreg = LogisticRegression()\n", + "# instantiate the model\n", + "logreg = LogisticRegression(solver='liblinear')\n", "\n", "# fit the model with data\n", "logreg.fit(X, y)\n", @@ -245,7 +254,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "*Image Credit: [Overfitting](http://commons.wikimedia.org/wiki/File:Overfitting.svg#/media/File:Overfitting.svg) by Chabacano. Licensed under GFDL via Wikimedia Commons.*" + "*Image Credit: [Overfitting](https://commons.wikimedia.org/wiki/File:Overfitting.svg#/media/File:Overfitting.svg) by Chabacano. Licensed under GFDL via Wikimedia Commons.*" ] }, { @@ -317,6 +326,15 @@ "cell_type": "code", "execution_count": 10, "metadata": {}, + "outputs": [], + "source": [ + "# added empty cell so that the cell numbering matches the video" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -335,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -355,39 +373,36 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", - " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", - " verbose=0, warm_start=False)" + "LogisticRegression(solver='liblinear')" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# STEP 2: train the model on the training set\n", - "logreg = LogisticRegression()\n", + "logreg = LogisticRegression(solver='liblinear')\n", "logreg.fit(X_train, y_train)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.95\n" + "0.9333333333333333\n" ] } ], @@ -408,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -435,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -462,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -478,27 +493,29 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Text(0,0.5,'Testing Accuracy')" + "Text(0, 0.5, 'Testing Accuracy')" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -533,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -542,7 +559,7 @@ "array([1])" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -580,8 +597,8 @@ "source": [ "## Resources\n", "\n", - "- Quora: [What is an intuitive explanation of overfitting?](http://www.quora.com/What-is-an-intuitive-explanation-of-overfitting/answer/Jessica-Su)\n", - "- Video: [Estimating prediction error](https://www.youtube.com/watch?v=_2ij6eaaSl0&t=2m34s) (12 minutes, starting at 2:34) by Hastie and Tibshirani\n", + "- Quora: [What is an intuitive explanation of overfitting?](https://www.quora.com/What-is-an-intuitive-explanation-of-over-fitting-particularly-with-a-small-sample-set-What-are-you-essentially-doing-by-over-fitting-How-does-the-over-promise-of-a-high-R%C2%B2-low-standard-error-occur/answer/Jessica-Su)\n", + "- Video: [Estimating prediction error](https://www.youtube.com/watch?v=_2ij6eaaSl0&list=PL5-da3qGB5IA6E6ZNXu7dp89_uv8yocmf&index=1&t=2m34s) (12 minutes, starting at 2:34) by Hastie and Tibshirani\n", "- [Understanding the Bias-Variance Tradeoff](http://scott.fortmann-roe.com/docs/BiasVariance.html)\n", " - [Guiding questions](https://github.com/justmarkham/DAT8/blob/master/homework/09_bias_variance.md) when reading this article\n", "- Video: [Visualizing bias and variance](http://work.caltech.edu/library/081.html) (15 minutes) by Abu-Mostafa" @@ -594,102 +611,9 @@ "## Comments or Questions?\n", "\n", "- Email: \n", - "- Website: http://dataschool.io\n", + "- Website: https://www.dataschool.io\n", "- Twitter: [@justmarkham](https://twitter.com/justmarkham)" ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ] } ], "metadata": { @@ -708,7 +632,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/06_linear_regression.ipynb b/06_linear_regression.ipynb index 89bdb77..5f8d3af 100644 --- a/06_linear_regression.ipynb +++ b/06_linear_regression.ipynb @@ -6,9 +6,9 @@ "source": [ "# Data science pipeline: pandas, seaborn, scikit-learn ([video #6](https://www.youtube.com/watch?v=3ZWuPVWq7p4&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=6))\n", "\n", - "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", + "Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", "\n", - "**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)." + "**Note:** This notebook uses Python 3.9.1 and scikit-learn 0.23.2. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16." ] }, { @@ -44,7 +44,16 @@ "**Pandas:** popular Python library for data exploration, manipulation, and analysis\n", "\n", "- Anaconda users: pandas is already installed\n", - "- Other users: [installation instructions](http://pandas.pydata.org/pandas-docs/stable/install.html)" + "- Other users: [installation instructions](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# added empty cell so that the cell numbering matches the video" ] }, { @@ -165,6 +174,15 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [], + "source": [ + "# added empty cell so that the cell numbering matches the video" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { @@ -242,7 +260,7 @@ "200 232.1 8.6 8.7 13.4" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -254,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -263,7 +281,7 @@ "(200, 4)" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -304,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -317,33 +335,35 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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"text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ "# visualize the relationship between the features and the response using scatterplots\n", - "sns.pairplot(data, x_vars=['TV','Radio','Newspaper'], y_vars='Sales', size=7, aspect=0.7, kind='reg')" + "sns.pairplot(data, x_vars=['TV','Radio','Newspaper'], y_vars='Sales', height=7, aspect=0.7, kind='reg')" ] }, { @@ -390,7 +410,25 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# added empty cell so that the cell numbering matches the video" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# added empty cell so that the cell numbering matches the video" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -463,7 +501,7 @@ "5 180.8 10.8 58.4" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -484,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -504,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -518,7 +556,7 @@ "Name: Sales, dtype: float64" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -536,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -563,7 +601,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -573,7 +611,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -604,16 +642,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" + "LinearRegression()" ] }, - "execution_count": 14, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -638,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -658,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -669,7 +707,7 @@ " ('Newspaper', 0.003450464711180378)]" ] }, - "execution_count": 16, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -705,7 +743,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -733,7 +771,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -753,7 +791,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -785,7 +823,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -816,7 +854,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -857,7 +895,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -885,7 +923,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -934,16 +972,16 @@ "\n", "Linear regression:\n", "\n", - "- [Longer notebook on linear regression](https://github.com/justmarkham/DAT5/blob/master/notebooks/09_linear_regression.ipynb) by me\n", - "- Chapter 3 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) and [related videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) by Hastie and Tibshirani (Stanford)\n", - "- [Quick reference guide to applying and interpreting linear regression](http://www.dataschool.io/applying-and-interpreting-linear-regression/) by me\n", + "- [Longer notebook on linear regression](https://github.com/justmarkham/DAT4/blob/master/notebooks/08_linear_regression.ipynb) by me\n", + "- Chapter 3 of [An Introduction to Statistical Learning](https://www.statlearning.com/) and [related videos](https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) by Hastie and Tibshirani (Stanford)\n", + "- [Quick reference guide to applying and interpreting linear regression](https://www.dataschool.io/applying-and-interpreting-linear-regression/) by me\n", "- [Introduction to linear regression](http://people.duke.edu/~rnau/regintro.htm) by Robert Nau (Duke)\n", "\n", "Pandas:\n", "\n", "- [pandas Q&A video series](https://www.dataschool.io/easier-data-analysis-with-pandas/) by me\n", "- [Three-part pandas tutorial](http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/) by Greg Reda\n", - "- [read_csv](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) and [read_table](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_table.html) documentation\n", + "- [read_csv](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) and [read_table](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_table.html) documentation\n", "\n", "Seaborn:\n", "\n", @@ -958,102 +996,9 @@ "## Comments or Questions?\n", "\n", "- Email: \n", - "- Website: http://dataschool.io\n", + "- Website: https://www.dataschool.io\n", "- Twitter: [@justmarkham](https://twitter.com/justmarkham)" ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ] } ], "metadata": { @@ -1072,7 +1017,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/07_cross_validation.ipynb b/07_cross_validation.ipynb index a03ad07..b0f8e28 100644 --- a/07_cross_validation.ipynb +++ b/07_cross_validation.ipynb @@ -6,9 +6,9 @@ "source": [ "# Cross-validation for parameter tuning, model selection, and feature selection ([video #7](https://www.youtube.com/watch?v=6dbrR-WymjI&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=7))\n", "\n", - "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", + "Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", "\n", - "**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)." + "**Note:** This notebook uses Python 3.9.1 and scikit-learn 0.23.2. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16." ] }, { @@ -34,7 +34,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Motivation:** Need a way to choose between machine learning models\n", + "**Motivation:** Need a way to choose between Machine Learning models\n", "\n", "- Goal is to estimate likely performance of a model on **out-of-sample data**\n", "\n", @@ -49,6 +49,15 @@ "- But, it provides a **high variance** estimate since changing which observations happen to be in the testing set can significantly change testing accuracy" ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# added empty cell so that the cell numbering matches the video" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -139,6 +148,24 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, + "outputs": [], + "source": [ + "# added empty cell so that the cell numbering matches the video" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# added empty cell so that the cell numbering matches the video" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -229,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -238,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -259,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -277,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -301,27 +328,29 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Text(0,0.5,'Cross-Validated Accuracy')" + "Text(0, 0.5, 'Cross-Validated Accuracy')" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -351,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -370,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -384,7 +413,7 @@ "source": [ "# 10-fold cross-validation with logistic regression\n", "from sklearn.linear_model import LogisticRegression\n", - "logreg = LogisticRegression()\n", + "logreg = LogisticRegression(solver='liblinear')\n", "print(cross_val_score(logreg, X, y, cv=10, scoring='accuracy').mean())" ] }, @@ -404,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -415,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -425,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -441,7 +470,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -462,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -482,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -502,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -520,7 +549,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -573,13 +602,13 @@ "source": [ "## Resources\n", "\n", - "- scikit-learn documentation: [Cross-validation](http://scikit-learn.org/stable/modules/cross_validation.html), [Model evaluation](http://scikit-learn.org/stable/modules/model_evaluation.html)\n", + "- scikit-learn documentation: [Cross-validation](https://scikit-learn.org/stable/modules/cross_validation.html), [Model evaluation](https://scikit-learn.org/stable/modules/model_evaluation.html)\n", "- scikit-learn issue on GitHub: [MSE is negative when returned by cross_val_score](https://github.com/scikit-learn/scikit-learn/issues/2439)\n", - "- Section 5.1 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) (11 pages) and related videos: [K-fold and leave-one-out cross-validation](https://www.youtube.com/watch?v=nZAM5OXrktY&list=PL5-da3qGB5IA6E6ZNXu7dp89_uv8yocmf) (14 minutes), [Cross-validation the right and wrong ways](https://www.youtube.com/watch?v=S06JpVoNaA0&list=PL5-da3qGB5IA6E6ZNXu7dp89_uv8yocmf) (10 minutes)\n", + "- Section 5.1 of [An Introduction to Statistical Learning](https://www.statlearning.com/) (11 pages) and related videos: [K-fold and leave-one-out cross-validation](https://www.youtube.com/watch?v=nZAM5OXrktY&list=PL5-da3qGB5IA6E6ZNXu7dp89_uv8yocmf) (14 minutes), [Cross-validation the right and wrong ways](https://www.youtube.com/watch?v=S06JpVoNaA0&list=PL5-da3qGB5IA6E6ZNXu7dp89_uv8yocmf) (10 minutes)\n", "- Scott Fortmann-Roe: [Accurately Measuring Model Prediction Error](http://scott.fortmann-roe.com/docs/MeasuringError.html)\n", - "- Machine Learning Mastery: [An Introduction to Feature Selection](http://machinelearningmastery.com/an-introduction-to-feature-selection/)\n", + "- Machine Learning Mastery: [An Introduction to Feature Selection](https://machinelearningmastery.com/an-introduction-to-feature-selection/)\n", "- Harvard CS109: [Cross-Validation: The Right and Wrong Way](https://github.com/cs109/content/blob/master/lec_10_cross_val.ipynb)\n", - "- Journal of Cheminformatics: [Cross-validation pitfalls when selecting and assessing regression and classification models](http://www.jcheminf.com/content/pdf/1758-2946-6-10.pdf)" + "- Journal of Cheminformatics: [Cross-validation pitfalls when selecting and assessing regression and classification models](https://jcheminf.biomedcentral.com/track/pdf/10.1186/1758-2946-6-10.pdf)" ] }, { @@ -589,102 +618,9 @@ "## Comments or Questions?\n", "\n", "- Email: \n", - "- Website: http://dataschool.io\n", + "- Website: https://www.dataschool.io\n", "- Twitter: [@justmarkham](https://twitter.com/justmarkham)" ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ] } ], "metadata": { @@ -703,7 +639,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/08_grid_search.ipynb b/08_grid_search.ipynb index aa0a82c..68cd609 100644 --- a/08_grid_search.ipynb +++ b/08_grid_search.ipynb @@ -6,9 +6,9 @@ "source": [ "# Efficiently searching for optimal tuning parameters ([video #8](https://www.youtube.com/watch?v=Gol_qOgRqfA&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=8))\n", "\n", - "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", + "Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", "\n", - "**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)." + "**Note:** This notebook uses Python 3.9.1 and scikit-learn 0.23.2. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16." ] }, { @@ -65,6 +65,15 @@ "**Goal:** Select the best tuning parameters (aka \"hyperparameters\") for KNN on the iris dataset" ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# added empty cell so that the cell numbering matches the video" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -163,7 +172,7 @@ { "data": { "text/plain": [ - "Text(0,0.5,'Cross-Validated Accuracy')" + "Text(0, 0.5, 'Cross-Validated Accuracy')" ] }, "execution_count": 7, @@ -172,12 +181,14 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -256,7 +267,7 @@ "outputs": [], "source": [ "# instantiate the grid\n", - "grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy', return_train_score=False)" + "grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy')" ] }, { @@ -274,14 +285,11 @@ { "data": { "text/plain": [ - "GridSearchCV(cv=10, error_score='raise',\n", - " estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", - " metric_params=None, n_jobs=1, n_neighbors=30, p=2,\n", - " weights='uniform'),\n", - " fit_params=None, iid=True, n_jobs=1,\n", - " param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]},\n", - " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", - " scoring='accuracy', verbose=0)" + "GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=30),\n", + " param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n", + " 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,\n", + " 23, 24, 25, 26, 27, 28, 29, 30]},\n", + " scoring='accuracy')" ] }, "execution_count": 12, @@ -606,7 +614,7 @@ { "data": { "text/plain": [ - "Text(0,0.5,'Cross-Validated Accuracy')" + "Text(0, 0.5, 'Cross-Validated Accuracy')" ] }, "execution_count": 16, @@ -615,12 +623,14 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -640,11 +650,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.98\n", + "0.9800000000000001\n", "{'n_neighbors': 13}\n", - "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", - " metric_params=None, n_jobs=1, n_neighbors=13, p=2,\n", - " weights='uniform')\n" + "KNeighborsClassifier(n_neighbors=13)\n" ] } ], @@ -709,14 +717,12 @@ { "data": { "text/plain": [ - "GridSearchCV(cv=10, error_score='raise',\n", - " estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", - " metric_params=None, n_jobs=1, n_neighbors=30, p=2,\n", - " weights='uniform'),\n", - " fit_params=None, iid=True, n_jobs=1,\n", - " param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], 'weights': ['uniform', 'distance']},\n", - " pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n", - " scoring='accuracy', verbose=0)" + "GridSearchCV(cv=10, estimator=KNeighborsClassifier(n_neighbors=30),\n", + " param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n", + " 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,\n", + " 23, 24, 25, 26, 27, 28, 29, 30],\n", + " 'weights': ['uniform', 'distance']},\n", + " scoring='accuracy')" ] }, "execution_count": 20, @@ -726,14 +732,16 @@ ], "source": [ "# instantiate and fit the grid\n", - "grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy', return_train_score=False)\n", + "grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy')\n", "grid.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 21, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { @@ -1271,7 +1279,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.98\n", + "0.9800000000000001\n", "{'n_neighbors': 13, 'weights': 'uniform'}\n" ] } @@ -1493,7 +1501,7 @@ ], "source": [ "# n_iter controls the number of searches\n", - "rand = RandomizedSearchCV(knn, param_dist, cv=10, scoring='accuracy', n_iter=10, random_state=5, return_train_score=False)\n", + "rand = RandomizedSearchCV(knn, param_dist, cv=10, scoring='accuracy', n_iter=10, random_state=5)\n", "rand.fit(X, y)\n", "pd.DataFrame(rand.cv_results_)[['mean_test_score', 'std_test_score', 'params']]" ] @@ -1507,7 +1515,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.98\n", + "0.9800000000000001\n", "{'weights': 'uniform', 'n_neighbors': 18}\n" ] } @@ -1527,7 +1535,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0.973, 0.98, 0.98, 0.98, 0.973, 0.98, 0.98, 0.973, 0.98, 0.973, 0.973, 0.98, 0.98, 0.98, 0.98, 0.973, 0.98, 0.98, 0.98, 0.973]\n" + "[0.98, 0.98, 0.98, 0.98, 0.973, 0.98, 0.973, 0.98, 0.98, 0.98, 0.973, 0.98, 0.98, 0.973, 0.973, 0.98, 0.98, 0.973, 0.973, 0.98]\n" ] } ], @@ -1535,7 +1543,7 @@ "# run RandomizedSearchCV 20 times (with n_iter=10) and record the best score\n", "best_scores = []\n", "for _ in range(20):\n", - " rand = RandomizedSearchCV(knn, param_dist, cv=10, scoring='accuracy', n_iter=10, return_train_score=False)\n", + " rand = RandomizedSearchCV(knn, param_dist, cv=10, scoring='accuracy', n_iter=10)\n", " rand.fit(X, y)\n", " best_scores.append(round(rand.best_score_, 3))\n", "print(best_scores)" @@ -1547,8 +1555,8 @@ "source": [ "## Resources\n", "\n", - "- scikit-learn documentation: [Grid search](http://scikit-learn.org/stable/modules/grid_search.html), [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html), [RandomizedSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html)\n", - "- Timed example: [Comparing randomized search and grid search](http://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html)\n", + "- scikit-learn documentation: [Grid search](https://scikit-learn.org/stable/modules/grid_search.html), [GridSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html), [RandomizedSearchCV](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html)\n", + "- Timed example: [Comparing randomized search and grid search](https://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html)\n", "- scikit-learn workshop by Andreas Mueller: [Video segment on randomized search](https://youtu.be/0wUF_Ov8b0A?t=17m38s) (3 minutes), [related notebook](https://github.com/amueller/pydata-nyc-advanced-sklearn/blob/master/Chapter%203%20-%20Randomized%20Hyper%20Parameter%20Search.ipynb)\n", "- Paper by Yoshua Bengio: [Random Search for Hyper-Parameter Optimization](http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf)" ] @@ -1560,102 +1568,9 @@ "## Comments or Questions?\n", "\n", "- Email: \n", - "- Website: http://dataschool.io\n", + "- Website: https://www.dataschool.io\n", "- Twitter: [@justmarkham](https://twitter.com/justmarkham)" ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ] } ], "metadata": { @@ -1674,7 +1589,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/09_classification_metrics.ipynb b/09_classification_metrics.ipynb index 4d37c9e..ae53318 100644 --- a/09_classification_metrics.ipynb +++ b/09_classification_metrics.ipynb @@ -6,9 +6,9 @@ "source": [ "# Evaluating a classification model ([video #9](https://www.youtube.com/watch?v=85dtiMz9tSo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=9))\n", "\n", - "Created by [Data School](http://www.dataschool.io/). Watch all 9 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", + "Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", "\n", - "**Note:** This notebook uses Python 3.6 and scikit-learn 0.19.1. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive)." + "**Note:** This notebook uses Python 3.9.1 and scikit-learn 0.23.2. The original notebook (shown in the video) used Python 2.7 and scikit-learn 0.16." ] }, { @@ -74,6 +74,15 @@ "[Pima Indians Diabetes dataset](https://www.kaggle.com/uciml/pima-indians-diabetes-database) originally from the UCI Machine Learning Repository" ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# added empty cell so that the cell numbering matches the video" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -246,10 +255,7 @@ { "data": { "text/plain": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", - " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", - " verbose=0, warm_start=False)" + "LogisticRegression(solver='liblinear')" ] }, "execution_count": 6, @@ -260,7 +266,7 @@ "source": [ "# train a logistic regression model on the training set\n", "from sklearn.linear_model import LogisticRegression\n", - "logreg = LogisticRegression()\n", + "logreg = LogisticRegression(solver='liblinear')\n", "logreg.fit(X_train, y_train)" ] }, @@ -579,7 +585,7 @@ } ], "source": [ - "print((TP + TN) / float(TP + TN + FP + FN))\n", + "print((TP + TN) / (TP + TN + FP + FN))\n", "print(metrics.accuracy_score(y_test, y_pred_class))" ] }, @@ -607,7 +613,7 @@ } ], "source": [ - "print((FP + FN) / float(TP + TN + FP + FN))\n", + "print((FP + FN) / (TP + TN + FP + FN))\n", "print(1 - metrics.accuracy_score(y_test, y_pred_class))" ] }, @@ -636,7 +642,7 @@ } ], "source": [ - "print(TP / float(TP + FN))\n", + "print(TP / (TP + FN))\n", "print(metrics.recall_score(y_test, y_pred_class))" ] }, @@ -663,7 +669,7 @@ } ], "source": [ - "print(TN / float(TN + FP))" + "print(TN / (TN + FP))" ] }, { @@ -687,7 +693,7 @@ } ], "source": [ - "print(FP / float(TN + FP))" + "print(FP / (TN + FP))" ] }, { @@ -714,7 +720,7 @@ } ], "source": [ - "print(TP / float(TP + FP))\n", + "print(TP / (TP + FP))\n", "print(metrics.precision_score(y_test, y_pred_class))" ] }, @@ -850,7 +856,7 @@ { "data": { "text/plain": [ - "Text(0,0.5,'Frequency')" + "Text(0, 0.5, 'Frequency')" ] }, "execution_count": 29, @@ -859,12 +865,14 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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+AvyM9PxnMzMbZqp+K+kl0qM9T+1sOGZmVreqfSXdR4t7ChGx2aBHZGZmtepPX0kNI4H9gVGDH46ZmdWt0j2GiHi88HokIk4E3tHZ0MzMrA5Vm5J2KIwuR7qCWL0jEZmZWa2qNiV9qzD8AnA/8K+DHo2ZmdWu6reSdut0IGZmNjRUbUr6997mR8S3ByccMzOrW3++lbQTMCOPvwuYDTzUiaDMzKw+/XlQzw4R8RSApKnAhRFxWKcCMzOzelTtEmNj4LnC+HNAz6BHY2Zmtat6xXA2cIOki0m/gH4PcFbHojIzs9pU/VbSVyT9HNg1Tzo0In7fubDMzKwuVZuSAFYBFkfEd4CHJW3aoZjMzKxGVR/teRxwFPCfedIKwDmdCsrMzOpT9YrhPcC7gWcAImI+fXSJIWkjSVdJmifpDkmfztNHSZop6e78d+1XswNmZja4qiaG5yIiyF1vS1q1wjIvAJ+NiNcDOwMflzSe9NCfWRExDpiVx83MbIiomhgukPR9YC1JhwNX0MdDeyJiQUTcnIefAuYBGwCTgOm52HRgvwHEbWZmHdLnt5IkCTgf2BJYDLwOODYiZlbdiKQeYHvgemC9iFgAKXlIWrfNMlOAKQAbb7xx1U2Zmdmr1GdiiIiQdElE7AhUTgYNklYDfgwcGRGLU57pW0ScApwCMGHChFc8Pc7MzDqjalPSdZJ26u/KJa1ASgo/jIif5MmPShqb548FFvZ3vWZm1jlVE8NupORwr6RbJd0m6dbeFshNUKcD85p6X50BTM7Dk4FL+xu0mZl1Tq9NSZI2jogHgb0HsO5dgIOB2yTNzdO+AEwj3cz+MPAg6fnRZmY2RPR1j+ESUq+qD0j6cUT8S9UVR8RvgHY3FHavuh4zM+uuvpqSigf2zToZiJmZDQ19JYZoM2xmZsNUX01J20paTLpyWDkPk8cjItboaHRmZtZ1vSaGiBjRrUDMzGxo6E+322ZmtgxwYjAzsxInBjMzK3FiMDOzEicGMzMrcWIwM7MSJwYzMytxYjAzsxInBjMzK3FiMDOzEicGMzMrcWIwM7MSJwYzMytxYjAzsxInBjMzK3FiMDOzEicGMzMrcWIwM7MSJwYzMytxYjAzsxInBjMzK3FiMDOzEicGMzMrcWIwM7MSJwYzMytxYjAzs5Ll6w7AzAZPz9GX1x0C90/bt+4Q7FXyFYOZmZU4MZiZWYkTg5mZlTgxmJlZiRODmZmVdCwxSDpD0kJJtxemjZI0U9Ld+e/andq+mZkNTCevGM4E9mqadjQwKyLGAbPyuJmZDSEdSwwRMRt4omnyJGB6Hp4O7Nep7ZuZ2cB0+x7DehGxACD/XbddQUlTJM2RNGfRokVdC9DMbFk3ZG8+R8QpETEhIiaMGTOm7nDMzJYZ3U4Mj0oaC5D/Luzy9s3MrA/dTgwzgMl5eDJwaZe3b2ZmfehYJ3qSzgUmAqMlPQwcB0wDLpD0YeBBYP/B2NZQ6DjMzJ9DGy46lhgi4v1tZu3eqW2amdmrN2RvPpuZWT2cGMzMrMSJwczMSpwYzMysxInBzMxKnBjMzKzEicHMzEqcGMzMrMSJwczMSpwYzMysxInBzMxKnBjMzKzEicHMzEqcGMzMrMSJwczMSpwYzMyspGMP6jGzZVPdT7K7f9q+tW5/OPAVg5mZlTgxmJlZiRODmZmVODGYmVmJE4OZmZU4MZiZWYkTg5mZlTgxmJlZiRODmZmVODGYmVmJE4OZmZU4MZiZWYkTg5mZlTgxmJlZiRODmZmVODGYmVmJE4OZmZU4MZiZWYkTg5mZldSSGCTtJekPku6RdHQdMZiZWWtdTwySRgD/C+wNjAfeL2l8t+MwM7PW6rhieCNwT0T8KSKeA84DJtUQh5mZtbB8DdvcAHioMP4w8KbmQpKmAFPy6D8k3d6F2JYGo4HH6g5iiHBdLOG6yPR110XB6wayUB2JQS2mxSsmRJwCnAIgaU5ETOh0YEsD18USroslXBdLuC6WkDRnIMvV0ZT0MLBRYXxDYH4NcZiZWQt1JIYbgXGSNpW0InAgMKOGOMzMrIWuNyVFxAuSPgH8EhgBnBERd/Sx2Cmdj2yp4bpYwnWxhOtiCdfFEgOqC0W8onnfzMyWYf7ls5mZlTgxmJlZyZBKDH11laHku3n+rZJ2qCPObqhQFwflOrhV0u8kbVtHnJ1WtfsUSTtJelHS+7oZXzdVqQtJEyXNlXSHpGu6HWO3VPj/WFPSTyXdkuvi0Dri7AZJZ0ha2O63XgM6bkbEkHiRbkTfC2wGrAjcAoxvKrMP8HPSbyF2Bq6vO+4a6+ItwNp5eO/hWBdV6qFQ7krgZ8D76o67xs/EWsCdwMZ5fN26466xLr4AfD0PjwGeAFasO/YO1cfbgB2A29vM7/dxcyhdMVTpKmMScFYk1wFrSRrb7UC7oM+6iIjfRcRf8uh1pN+DDDdVu0/5JPBjYGE3g+uyKnXxAeAnEfEgQEQM1/qoUhcBrC5JwGqkxPBCd8PsjoiYTdq/dvp93BxKiaFVVxkbDKDMcNDf/fww6YxguOmzHiRtALwHOLmLcdWhymdiC2BtSVdLuknSB7sWXXdVqYuTgNeTfjx7G/DpiHipO+ENOf0+btbRJUY7VbrKqNSdxjBQeT8l7UZKDG/taET1qFIPJwJHRcSL6eRw2KpSF8sDOwK7AysD10q6LiL+2OnguqxKXfwzMBd4B7A5MFPSryNicYdjG4r6fdwcSomhSlcZy0p3GpX2U9I2wGnA3hHxeJdi66Yq9TABOC8nhdHAPpJeiIhLuhJh91T9/3gsIp4BnpE0G9gWGG6JoUpdHApMi9TIfo+k+4AtgRu6E+KQ0u/j5lBqSqrSVcYM4IP5LvvOwF8jYkG3A+2CPutC0sbAT4CDh+EZYUOf9RARm0ZET0T0ABcBHxuGSQGq/X9cCuwqaXlJq5B6LZ7X5Ti7oUpdPEi6ckLSeqReRv/U1SiHjn4fN4fMFUO06SpD0hF5/smkb53sA9wD/I10VjDsVKyLY4F1gO/ls+UXYpj1KFmxHpYJVeoiIuZJ+gVwK/AScFpEDLvu6it+Lr4EnCnpNlJTylERMSy74pZ0LjARGC3pYeA4YAUY+HHTXWKYmVnJUGpKMjOzIcCJwczMSpwYzMysxInBzMxKnBjMzKzEiWEZl3sknSvpdkkX5u+/D3RdZzZ6N5V0mqTxvZSdKOktA9jG/ZJGDzTGwVqvpKmSPtdi+vqSLsrDEyVdloff3egFVNJ+vdVNP+PeMr9/v5e0eS/lDpF0Uh4+oq/uMorvZcU4eiR9oHrkNpQ5MdizEbFdRGwNPAccUZwpacRAVhoRh0XEnb0UmUjqIbZrJHX8dzsRMT8iXnFAjYgZETEtj+4HDEpiyOu6NCK2j4h7K8Z4ckScNUjbb+ghdeJnw4ATgxX9GnhtPtO9StKPgNskjZD0DUk35v7cPwIv9/N+kqQ7JV0OrNtYUe7IbUIe3kvSzUp948+S1ENKQJ/JZ7u7Shoj6cd5GzdK2iUvu46kX+Uz4u/Tut8XJD0t6Vt5O7MkjSnE8VWlZxN8WtLueV23KfVjv1JhNZ+XdEN+vTYv/y5J1+dlrsi/om3YVtKVku6WdHgu36MW/eI3ztjzVdK7gW/kfd9c0s2FcuMk3dRi+e0kXZfr/2JJa0vaBzgSOEzSVS2WOVTSH/O+71KY/vLVjqTDc33fkuu/eMW4h6Rf53W8M5dv+VkAppF+dT1X0md6+cyMlTRbS65Sd231flrN6u5L3K96X8DT+e/ypC4VPko6m38G2DTPmwJ8MQ+vBMwBNgXeC8wk/fp0feBJ8vMQgKtJ/RiNIfXs2FjXqPx3KvC5Qhw/At6ahzcG5uXh7wLH5uF9SZ1/jW6xHwEclIePBU4qxPG9PDwyx7JFHj8LODIP3w/8Vx7+IHBZHl6bJT8EPQz4ViH+W0id1Y3O612fdOZ8ey4zsbCeQwoxnUnhuRHAVcB2efirwCdb7N+twNvz8P8DTmxVj4XyY0ndQowhPbPgt4Xtv7wMsE5hmS83tp1j/AXp5HEcqb+dkbT/LLy8r318Zj5bqOcRwOp1/w/49crXkOkSw2qzsqS5efjXwOmkJp4bIuK+PP2fgG0Kbc5rkg4WbwPOjYgXgfmSrmyx/p2B2Y11RUS7fuP3AMZrSQ+pa0haPW/jvXnZyyX9pc3yLwHn5+FzSP1INTSmvw64L5b0LTUd+Diph1aAcwt/T8jDGwLnK/VfvyLQqBNITTjPAs/mM/Y3knr07K/TgEMl/TtwQF7PyyStCawVEY0nsk0HLuxjnW8Cro6IRXkd55O65W62taQvkx7ysxqpm4mGCyJ1VX23pD+ROqFr91l4rmm97crdCJwhaQXgkoiY28d+WA2cGOzZiNiuOCEfnJ8pTiKdSf6yqdw+9N3tuSqUgXRm+uZ8oG2OZSD9thSXaexLX/1yR4vh/wG+HREzJE0knW23Kt9qvKofk/q3uRK4KQavp9wq8ZwJ7BcRt0g6hHTm3275oP1nYWJT2Zblctm3ka7+zpb0jRj8+x32Kvkeg1XxS+Cj+SwPSVtIWhWYDRyY25PHAru1WPZa4O2SNs3LjsrTnwJWL5T7FfCJxoik7fLgbOCgPG1vUtNOK8sBjbPTDwC/aVHmLqCncf8AOBgoPhf5gMLfa/PwmsAjeXhy0/omSRopaR3SAfXGNrE1K+17RPydVMf/B/yguXBE/BX4S6E9vjnuVq4HJuZ7NCsA+7cptzqwIJc5qGne/pKWU/q202bAH2j/WWh+P1uWk7QJsDAiTiVdnQ7b57YvzXzFYFWcRmo7v1npFH4R6dswF5MehHIbqc//VxysImKRpCnATyQtR3r85p7AT4GLJE0iPZrzU8D/SrqV9LmcTbpBfTxwbr5Bew2p3byVZ4Ct8o3bv7LkIF+M5e9KD4W/UOkbSjdSfvLbSpKuJyWZ9+dpU3P5R0iPUN20UP4G4HLSPZEvRcR8pRvrfTkPOFXSp0j3Gu4FfkhqMvtVm2UmAyfnm8N/oo8eMiNigaSppAS3ALiZ1Kbf7BhSEnmA9D4WD+5/INX5esARuf7afRZuBV6QdAvpKuQ7bcpNJN3kfx54mnQ/x4YY965qw4KkpyNitbrjGKj8LaE1I+KYumMx8xWDWc0kXUx6/OQ76o7FDHzFYGZmTXzz2czMSpwYzMysxInBzMxKnBjMzKzEicHMzEr+PzwCc+ALOkLRAAAAAElFTkSuQmCC\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -892,7 +900,7 @@ "source": [ "# predict diabetes if the predicted probability is greater than 0.3\n", "from sklearn.preprocessing import binarize\n", - "y_pred_class = binarize([y_pred_prob], 0.3)[0]" + "y_pred_class = binarize([y_pred_prob], threshold=0.3)[0]" ] }, { @@ -991,7 +999,7 @@ ], "source": [ "# sensitivity has increased (used to be 0.24)\n", - "print(46 / float(46 + 16))" + "print(46 / (46 + 16))" ] }, { @@ -1009,7 +1017,7 @@ ], "source": [ "# specificity has decreased (used to be 0.91)\n", - "print(80 / float(80 + 50))" + "print(80 / (80 + 50))" ] }, { @@ -1041,12 +1049,14 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1195,7 +1205,7 @@ "source": [ "## Confusion Matrix Resources\n", "\n", - "- Blog post: [Simple guide to confusion matrix terminology](http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) by me\n", + "- Blog post: [Simple guide to confusion matrix terminology](https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/) by me\n", "- Videos: [Intuitive sensitivity and specificity](https://www.youtube.com/watch?v=U4_3fditnWg) (9 minutes) and [The tradeoff between sensitivity and specificity](https://www.youtube.com/watch?v=vtYDyGGeQyo) (13 minutes) by Rahul Patwari\n", "- Notebook: [How to calculate \"expected value\"](https://github.com/podopie/DAT18NYC/blob/master/classes/13-expected_value_cost_benefit_analysis.ipynb) from a confusion matrix by treating it as a cost-benefit matrix (by Ed Podojil)\n", "- Graphic: How [classification threshold](https://media.amazonwebservices.com/blog/2015/ml_adjust_model_1.png) affects different evaluation metrics (from a [blog post](https://aws.amazon.com/blogs/aws/amazon-machine-learning-make-data-driven-decisions-at-scale/) about Amazon Machine Learning)\n", @@ -1203,16 +1213,16 @@ "\n", "## ROC and AUC Resources\n", "\n", - "- Video: [ROC Curves and Area Under the Curve](https://www.youtube.com/watch?v=OAl6eAyP-yo) (14 minutes) by me, including [transcript and screenshots](http://www.dataschool.io/roc-curves-and-auc-explained/) and a [visualization](http://www.navan.name/roc/)\n", + "- Video: [ROC Curves and Area Under the Curve](https://www.youtube.com/watch?v=OAl6eAyP-yo) (14 minutes) by me, including [transcript and screenshots](https://www.dataschool.io/roc-curves-and-auc-explained/) and a [visualization](http://www.navan.name/roc/)\n", "- Video: [ROC Curves](https://www.youtube.com/watch?v=21Igj5Pr6u4) (12 minutes) by Rahul Patwari\n", "- Paper: [An introduction to ROC analysis](http://people.inf.elte.hu/kiss/13dwhdm/roc.pdf) by Tom Fawcett\n", - "- Usage examples: [Comparing different feature sets](http://research.microsoft.com/pubs/205472/aisec10-leontjeva.pdf) for detecting fraudulent Skype users, and [comparing different classifiers](http://www.cse.ust.hk/nevinZhangGroup/readings/yi/Bradley_PR97.pdf) on a number of popular datasets\n", + "- Usage examples: [Comparing different feature sets](https://www.microsoft.com/en-us/research/wp-content/uploads/2013/11/aisec10-leontjeva.pdf) for detecting fraudulent Skype users, and [comparing different classifiers](https://www.cse.ust.hk/nevinZhangGroup/readings/yi/Bradley_PR97.pdf) on a number of popular datasets\n", "\n", "## Other Resources\n", "\n", - "- scikit-learn documentation: [Model evaluation](http://scikit-learn.org/stable/modules/model_evaluation.html)\n", + "- scikit-learn documentation: [Model evaluation](https://scikit-learn.org/stable/modules/model_evaluation.html)\n", "- Guide: [Comparing model evaluation procedures and metrics](https://github.com/justmarkham/DAT8/blob/master/other/model_evaluation_comparison.md) by me\n", - "- Video: [Counterfactual evaluation of machine learning models](https://www.youtube.com/watch?v=QWCSxAKR-h0) (45 minutes) about how Stripe evaluates its fraud detection model, including [slides](http://www.slideshare.net/MichaelManapat/counterfactual-evaluation-of-machine-learning-models)" + "- Video: [Counterfactual evaluation of machine learning models](https://www.youtube.com/watch?v=QWCSxAKR-h0) (45 minutes) about how Stripe evaluates its fraud detection model, including [slides](https://www.slideshare.net/MichaelManapat/counterfactual-evaluation-of-machine-learning-models)" ] }, { @@ -1222,102 +1232,9 @@ "## Comments or Questions?\n", "\n", "- Email: \n", - "- Website: http://dataschool.io\n", + "- Website: https://www.dataschool.io\n", "- Twitter: [@justmarkham](https://twitter.com/justmarkham)" ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.core.display import HTML\n", - "def css_styling():\n", - " styles = open(\"styles/custom.css\", \"r\").read()\n", - " return HTML(styles)\n", - "css_styling()" - ] } ], "metadata": { @@ -1336,7 +1253,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/10_categorical_features.ipynb b/10_categorical_features.ipynb index 0f434f1..76b599a 100644 --- a/10_categorical_features.ipynb +++ b/10_categorical_features.ipynb @@ -4,11 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Encoding categorical features ([video #10](https://www.youtube.com/watch?v=irHhDMbw3xo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=10))\n", + "# Building a Machine Learning workflow ([video #10](https://www.youtube.com/watch?v=irHhDMbw3xo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=10))\n", "\n", "Created by [Data School](https://www.dataschool.io). Watch all 10 videos on [YouTube](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A). Download the notebooks from [GitHub](https://github.com/justmarkham/scikit-learn-videos).\n", "\n", - "**Note:** This notebook uses scikit-learn 0.20. Some of the code below will not work if you are using an earlier version of scikit-learn." + "**Note:** This notebook uses Python 3.9.1 and scikit-learn 0.23.2. The original notebook (shown in the video) used Python 3.7 and scikit-learn 0.20.2." ] }, { @@ -297,33 +297,71 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "(889, 1)\n", - "(889,)\n" - ] + "data": { + "text/plain": [ + "(889, 1)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print(X.shape)\n", - "print(y.shape)" + "X.shape" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(889,)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "from sklearn.linear_model import LogisticRegression\n", - "logreg = LogisticRegression(solver='lbfgs')" + "y.shape" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LogisticRegression" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "logreg = LogisticRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import cross_val_score" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, "outputs": [ { "data": { @@ -331,19 +369,18 @@ "0.6783406335301212" ] }, - "execution_count": 13, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from sklearn.model_selection import cross_val_score\n", "cross_val_score(logreg, X, y, cv=5, scoring='accuracy').mean()" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -354,7 +391,7 @@ "Name: Survived, dtype: float64" ] }, - "execution_count": 14, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -372,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -451,7 +488,7 @@ "4 0 3 male S" ] }, - "execution_count": 15, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -462,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -473,7 +510,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -488,7 +525,7 @@ " [0., 1.]])" ] }, - "execution_count": 17, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -499,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -508,7 +545,7 @@ "[array(['female', 'male'], dtype=object)]" ] }, - "execution_count": 18, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -519,7 +556,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -534,7 +571,7 @@ " [0., 1., 0.]])" ] }, - "execution_count": 19, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -545,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -554,7 +591,7 @@ "[array(['C', 'Q', 'S'], dtype=object)]" ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -563,32 +600,6 @@ "ohe.categories_" ] }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0., 1., 0., 0., 1.],\n", - " [1., 0., 1., 0., 0.],\n", - " [1., 0., 0., 0., 1.],\n", - " ...,\n", - " [1., 0., 0., 0., 1.],\n", - " [0., 1., 1., 0., 0.],\n", - " [0., 1., 0., 1., 0.]])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ohe.fit_transform(df[['Sex', 'Embarked']])" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -598,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -607,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -680,7 +691,7 @@ "4 3 male S" ] }, - "execution_count": 23, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -691,7 +702,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -701,7 +712,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -712,7 +723,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -727,7 +738,7 @@ " [0., 1., 0., 1., 0., 3.]])" ] }, - "execution_count": 26, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -738,7 +749,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -748,7 +759,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -757,7 +768,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -766,7 +777,7 @@ "0.7727924839713071" ] }, - "execution_count": 29, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -786,7 +797,16 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# added empty cell so that the cell numbering matches the video" + ] + }, + { + "cell_type": "code", + "execution_count": 33, "metadata": { "scrolled": true }, @@ -861,7 +881,7 @@ "790 3 male Q" ] }, - "execution_count": 30, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -873,7 +893,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 34, "metadata": { "scrolled": true }, @@ -881,15 +901,15 @@ { "data": { "text/plain": [ - "Pipeline(memory=None,\n", - " steps=[('columntransformer', ColumnTransformer(n_jobs=None, remainder='passthrough', sparse_threshold=0.3,\n", - " transformer_weights=None,\n", - " transformers=[('onehotencoder', OneHotEncoder(categorical_features=None, categories=None,\n", - " dtype=, handle_unknown='error...enalty='l2', random_state=None, solver='lbfgs',\n", - " tol=0.0001, verbose=0, warm_start=False))])" + "Pipeline(steps=[('columntransformer',\n", + " ColumnTransformer(remainder='passthrough',\n", + " transformers=[('onehotencoder',\n", + " OneHotEncoder(),\n", + " ['Sex', 'Embarked'])])),\n", + " ('logisticregression', LogisticRegression())])" ] }, - "execution_count": 31, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -900,7 +920,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -909,7 +929,7 @@ "array([1, 0, 1, 1, 0])" ] }, - "execution_count": 32, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -927,7 +947,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -941,7 +961,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -953,7 +973,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -965,67 +985,13 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "pipe = make_pipeline(column_trans, logreg)" ] }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7727924839713071" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cross_val_score(pipe, X, y, cv=5, scoring='accuracy').mean()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "X_new = X.sample(5, random_state=99)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(memory=None,\n", - " steps=[('columntransformer', ColumnTransformer(n_jobs=None, remainder='passthrough', sparse_threshold=0.3,\n", - " transformer_weights=None,\n", - " transformers=[('onehotencoder', OneHotEncoder(categorical_features=None, categories=None,\n", - " dtype=, handle_unknown='error...enalty='l2', random_state=None, solver='lbfgs',\n", - " tol=0.0001, verbose=0, warm_start=False))])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipe.fit(X, y)" - ] - }, { "cell_type": "code", "execution_count": 40, @@ -1034,7 +1000,7 @@ { "data": { "text/plain": [ - "array([1, 0, 1, 1, 0])" + "0.7727924839713071" ] }, "execution_count": 40, @@ -1043,8 +1009,49 @@ } ], "source": [ + "cross_val_score(pipe, X, y, cv=5, scoring='accuracy').mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "X_new = X.sample(5, random_state=99)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 0, 1, 1, 0])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe.fit(X, y)\n", "pipe.predict(X_new)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comments or Questions?\n", + "\n", + "- Email: \n", + "- Website: https://www.dataschool.io\n", + "- Twitter: [@justmarkham](https://twitter.com/justmarkham)" + ] } ], "metadata": { @@ -1063,7 +1070,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/README.md b/README.md index e78e874..fc6b2d7 100644 --- a/README.md +++ b/README.md @@ -1,41 +1,41 @@ -# Introduction to machine learning with scikit-learn +# Introduction to Machine Learning with scikit-learn -This video series will teach you how to solve machine learning problems using Python's popular scikit-learn library. There are **10 video tutorials** totaling 4.5 hours, each with a corresponding **Jupyter notebook**. The notebook contains everything you see in the video: code, output, images, and comments. +This video series will teach you how to solve Machine Learning problems using Python's popular scikit-learn library. There are **10 video tutorials** totaling 4.5 hours, each with a corresponding **Jupyter notebook**. The notebook contains everything you see in the video: code, output, images, and comments. -**Note:** The notebooks in this repository have been updated to use Python 3.6 and scikit-learn 0.19.1. The original notebooks (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive). You can read about how I updated the code in this [blog post](https://www.dataschool.io/how-to-update-your-scikit-learn-code-for-2018/). +**Note:** The notebooks in this repository have been updated to use Python 3.9.1 and scikit-learn 0.23.2. The original notebooks (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive). You can read about how I updated the code in this [blog post](https://www.dataschool.io/how-to-update-your-scikit-learn-code-for-2018/). You can [watch the entire series](https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A) on YouTube, and [view all of the notebooks](http://nbviewer.jupyter.org/github/justmarkham/scikit-learn-videos/tree/master/) using nbviewer. [![Watch the first tutorial video](images/youtube.png)](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1 "Watch the first tutorial video") -Once you complete this video series, I recommend enrolling in my online course, [Machine Learning with Text in Python](http://www.dataschool.io/learn/), to gain a deeper understanding of scikit-learn and Natural Language Processing. +Once you complete this video series, I recommend enrolling in my online course, [Machine Learning with Text in Python](https://www.dataschool.io/learn/), to gain a deeper understanding of scikit-learn and Natural Language Processing. ## Table of Contents -1. What is machine learning, and how does it work? ([video](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1), [notebook](01_machine_learning_intro.ipynb)) - - What is machine learning? - - What are the two main categories of machine learning? - - What are some examples of machine learning? - - How does machine learning "work"? +1. What is Machine Learning, and how does it work? ([video](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1), [notebook](01_machine_learning_intro.ipynb)) + - What is Machine Learning? + - What are the two main categories of Machine Learning? + - What are some examples of Machine Learning? + - How does Machine Learning "work"? -2. Setting up Python for machine learning: scikit-learn and Jupyter Notebook ([video](https://www.youtube.com/watch?v=IsXXlYVBt1M&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=2), [notebook](02_machine_learning_setup.ipynb)) +2. Setting up Python for Machine Learning: scikit-learn and Jupyter Notebook ([video](https://www.youtube.com/watch?v=IsXXlYVBt1M&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=2), [notebook](02_machine_learning_setup.ipynb)) - What are the benefits and drawbacks of scikit-learn? - How do I install scikit-learn? - How do I use the Jupyter Notebook? - What are some good resources for learning Python? 3. Getting started in scikit-learn with the famous iris dataset ([video](https://www.youtube.com/watch?v=hd1W4CyPX58&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=3), [notebook](03_getting_started_with_iris.ipynb)) - - What is the famous iris dataset, and how does it relate to machine learning? + - What is the famous iris dataset, and how does it relate to Machine Learning? - How do we load the iris dataset into scikit-learn? - - How do we describe a dataset using machine learning terminology? + - How do we describe a dataset using Machine Learning terminology? - What are scikit-learn's four key requirements for working with data? -4. Training a machine learning model with scikit-learn ([video](https://www.youtube.com/watch?v=RlQuVL6-qe8&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=4), [notebook](04_model_training.ipynb)) +4. Training a Machine Learning model with scikit-learn ([video](https://www.youtube.com/watch?v=RlQuVL6-qe8&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=4), [notebook](04_model_training.ipynb)) - What is the K-nearest neighbors classification model? - What are the four steps for model training and prediction in scikit-learn? - - How can I apply this pattern to other machine learning models? + - How can I apply this pattern to other Machine Learning models? -5. Comparing machine learning models in scikit-learn ([video](https://www.youtube.com/watch?v=0pP4EwWJgIU&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=5), [notebook](05_model_evaluation.ipynb)) +5. Comparing Machine Learning models in scikit-learn ([video](https://www.youtube.com/watch?v=0pP4EwWJgIU&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=5), [notebook](05_model_evaluation.ipynb)) - How do I choose which model to use for my supervised learning task? - How do I choose the best tuning parameters for that model? - How do I estimate the likely performance of my model on out-of-sample data? @@ -70,7 +70,7 @@ Once you complete this video series, I recommend enrolling in my online course, - What is the purpose of an ROC curve? - How does Area Under the Curve (AUC) differ from classification accuracy? -10. Encoding categorical features ([video](https://www.youtube.com/watch?v=irHhDMbw3xo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=10), [notebook](10_categorical_features.ipynb)) +10. Building a Machine Learning workflow ([video](https://www.youtube.com/watch?v=irHhDMbw3xo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=10), [notebook](10_categorical_features.ipynb)) - Why should you use a Pipeline? - How do you encode categorical features with OneHotEncoder? - How do you apply OneHotEncoder to selected columns with ColumnTransformer? @@ -80,7 +80,7 @@ Once you complete this video series, I recommend enrolling in my online course, ## Bonus Video -At the PyCon 2016 conference, I taught a **3-hour tutorial** that builds upon this video series and focuses on **text-based data**. You can watch the [tutorial video](https://www.youtube.com/watch?v=ZiKMIuYidY0&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=10) on YouTube. +At the PyCon 2016 conference, I taught a **3-hour tutorial** that builds upon this video series and focuses on **text-based data**. You can watch the [tutorial video](https://www.youtube.com/watch?v=ZiKMIuYidY0&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=11) on YouTube. Here are the topics I covered: