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‎data_science/ensemble/randomforest.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn import datasets\n",
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"from sklearn import tree\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.model_selection import cross_val_score\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Load MNIST dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"mnist = datasets.load_digits()\n",
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"features, labels = mnist.data, mnist.target"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Cross Validation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def cross_validation(classifier,features, labels):\n",
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" cv_scores = []\n",
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"\n",
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" for i in range(10):\n",
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" scores = cross_val_score(classifier, features, labels, cv=10, scoring='accuracy')\n",
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" cv_scores.append(scores.mean())\n",
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" \n",
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" return cv_scores"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"dt_cv_scores = cross_validation(tree.DecisionTreeClassifier(), features, labels)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"rf_cv_scores = cross_validation(RandomForestClassifier(), features, labels)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Random Forest VS Decision Tree visualization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"cv_list = [ \n",
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" ['random_forest',rf_cv_scores],\n",
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" ['decision_tree',dt_cv_scores],\n",
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" ]\n",
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"df = pd.DataFrame.from_items(cv_list)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x1a1ddd37f0>"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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\n",
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"text/plain": [
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"<matplotlib.figure.Figure at 0x1189aa588>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"df.plot()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Decision Tree Accuracy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.8343173330831328"
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]
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},
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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.mean(dt_cv_scores)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Random Forest Accuracy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.9223850187122359"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.mean(rf_cv_scores)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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