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‎SKL-Binary- Accuracy.ipynb

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{
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"cells": [
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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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"Appraising Binary Classification Models\n",
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"======================================\n",
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"Interesting Theory Stuff : Accuracy, Precision, F-Measures, Recall, Specificity, Sensitivity\n"
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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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"### Set up some data"
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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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"import numpy as np\n",
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"\n",
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"y_pred = np.zeros(10000)\n",
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"y_test = np.zeros(10000)\n",
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"\n",
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"indices1 = np.random.randint(0,10000,300)\n",
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"indices2 = np.random.randint(0,10000,400)\n",
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"indices3 = np.random.randint(0,10000,500)\n",
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"y_pred[indices1] = 1\n",
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"y_test[indices2] = 1\n",
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"y_pred[indices3] = 1\n",
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"y_test[indices3] = 1\n"
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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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"858.0"
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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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"source": [
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"np.sum(y_test)"
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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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"774.0"
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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.sum(y_pred)"
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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": 23,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy score: 0.939\n"
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]
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}
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],
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"source": [
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"from sklearn.metrics import accuracy_score\n",
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"print('Accuracy score: ', accuracy_score(y_test, y_pred)) \n"
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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": 24,
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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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"array([[8879, 263],\n",
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" [ 347, 511]])"
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]
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},
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"execution_count": 24,
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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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"from sklearn.metrics import confusion_matrix\n",
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"\n",
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"confusion_matrix(y_test,y_pred)"
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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": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" precision recall f1-score support\n",
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"\n",
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" 0.0 0.96 0.97 0.97 9137\n",
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" 1.0 0.65 0.58 0.62 863\n",
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"\n",
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"avg / total 0.93 0.94 0.94 10000\n",
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"\n"
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]
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}
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],
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"source": [
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"from sklearn.metrics import classification_report\n",
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"print(classification_report(y_test, y_pred))"
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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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.metrics import precision_recall_curve\n",
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"\n",
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"precision, recall, thresholds = precision_recall_curve(y_test, y_pred)"
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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": 13,
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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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"array([ 0.0863 , 0.65116279, 1. ])"
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]
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},
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"execution_count": 13,
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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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"precision"
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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": 14,
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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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"array([ 1. , 0.58400927, 0. ])"
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]
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},
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"execution_count": 14,
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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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"recall"
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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": 15,
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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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"array([ 0., 1.])"
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]
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},
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"execution_count": 15,
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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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"thresholds"
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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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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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 3.6",
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"language": "python",
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"name": "python36"
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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": "3.6.2"
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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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