webinars
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@ -281,6 +281,7 @@ _Note: Below you can find the best lectures for popular Machine Learning Algorit
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- [CS131 Computer Vision: Foundations and Applications Fall 2019](http://vision.stanford.edu/teaching/cs131_fall1920/index.html)
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- [CS131 Computer Vision: Foundations and Applications Fall 2019](http://vision.stanford.edu/teaching/cs131_fall1920/index.html)
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- [CS231A: Computer Vision, From 3D Reconstruction to Recognition Winter 2018](http://web.stanford.edu/class/cs231a/)
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- [CS231A: Computer Vision, From 3D Reconstruction to Recognition Winter 2018](http://web.stanford.edu/class/cs231a/)
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- [CS231n Convolutional Neural Networks for Visual Recognition](https://cs231n.github.io/)
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### CNN
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### CNN
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@ -5,3 +5,7 @@
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## [waymo-open-dataset](https://github.com/waymo-research/waymo-open-dataset)
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## [waymo-open-dataset](https://github.com/waymo-research/waymo-open-dataset)
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Waymo Open Dataset https://www.waymo.com/open
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Waymo Open Dataset https://www.waymo.com/open
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### MIT 6.S094: Deep Learning for Self-Driving Cars
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- [DeepTraffic](https://selfdrivingcars.mit.edu/deeptraffic/)
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@ -36,6 +36,11 @@ Look at the papers I mentioned. Links in the link below
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- Papers - https://github.com/terryum/awesome-deep-learning-papers#new-papers
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- Papers - https://github.com/terryum/awesome-deep-learning-papers#new-papers
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- Ai blog - https://ai.googleblog.com/
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- Ai blog - https://ai.googleblog.com/
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## Demo
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- [Image-to-Image Translation with Conditional Adversarial Nets](https://phillipi.github.io/pix2pix/)
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- [Image-to-Image Demo](https://affinelayer.com/pixsrv/) _Interactive Image Translation with pix2pix-tensorflow_
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# Courses
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# Courses
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- [A to Z ML/DL](https://www.udemy.com/course/machinelearning/)
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- [A to Z ML/DL](https://www.udemy.com/course/machinelearning/)
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Ensemble Methods.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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},
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"cells": [
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{
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"cell_type": "code",
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"metadata": {
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"id": "-mCmaGdYCtBc",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"# Load Library\n",
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"from sklearn.datasets import make_moons\n",
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"from sklearn.metrics import accuracy_score\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.tree import DecisionTreeClassifier\n",
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"from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier,GradientBoostingClassifier"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "4ja4yb4_CzlY",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"# Step1: Create data set\n",
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"X, y = make_moons(n_samples=10000, noise=.5, random_state=0)"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "WbIUBF8pdXUF",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"# Step2: Split the training test set\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "8vRma5CzC1id",
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"colab_type": "code",
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"outputId": "47d557ac-f8d7-4b3d-8fb8-94893afbc5fd",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 51
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}
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},
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"source": [
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"# Step 3: Fit a Decision Tree model as comparison\n",
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"clf = DecisionTreeClassifier()\n",
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"clf.fit(X_train, y_train)\n",
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"print(\" Train Accuracy\", accuracy_score(y_train, clf.predict(X_train)))\n",
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"print(\" Test Accuracy\", accuracy_score(y_test, clf.predict(X_test)))"
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],
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"execution_count": 15,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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" Train Accuracy 1.0\n",
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" Test Accuracy 0.751\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "LeoG6gvlC1oR",
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"colab_type": "code",
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"outputId": "42772dfc-410f-474d-c442-a940260ac783",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 51
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}
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},
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"source": [
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"# Step 4: Fit a Random Forest model, \" compared to \"Decision Tree model, accuracy go up by 5%\n",
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"clf = RandomForestClassifier(n_estimators=100, max_features=\"auto\",random_state=0)\n",
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"clf.fit(X_train, y_train)\n",
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"print(\" Train Accuracy\", accuracy_score(y_train, clf.predict(X_train)))\n",
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"print(\" Test Accuracy\", accuracy_score(y_test, clf.predict(X_test)))"
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],
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"execution_count": 16,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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" Train Accuracy 1.0\n",
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" Test Accuracy 0.7965\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "h_ntW2CiC1ll",
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"colab_type": "code",
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"outputId": "753a8327-e25f-4c2d-9d39-e3ee62c13192",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 51
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}
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},
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"source": [
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"# Step 5: Fit a AdaBoost model, \" compared to \"Decision Tree model, accuracy go up by 10%\n",
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"clf = AdaBoostClassifier(n_estimators=100)\n",
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"clf.fit(X_train, y_train)\n",
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"print(\" Train Accuracy\", accuracy_score(y_train, clf.predict(X_train)))\n",
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"print(\" Test Accuracy\", accuracy_score(y_test, clf.predict(X_test)))"
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],
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"execution_count": 17,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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" Train Accuracy 0.825625\n",
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" Test Accuracy 0.833\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "GXw2cMWuC92S",
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"colab_type": "code",
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"outputId": "ce935fdf-df0a-4405-e6d3-a41af02a7ae6",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 51
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}
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},
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"source": [
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"# Step 6: Fit a Gradient Boosting model, \" compared to \"Decision Tree model, accuracy go up by 10%\n",
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"clf = GradientBoostingClassifier(n_estimators=100)\n",
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"clf.fit(X_train, y_train)\n",
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"print(\" Train Accuracy\", accuracy_score(y_train, clf.predict(X_train)))\n",
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"print(\" Test Accuracy\", accuracy_score(y_test, clf.predict(X_test)))"
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],
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"execution_count": 18,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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" Train Accuracy 0.829875\n",
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" Test Accuracy 0.8335\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "QY37GWzl9CQD",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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""
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],
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"execution_count": 0,
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"outputs": []
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}
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]
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}
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