142 lines
2.8 KiB
Plaintext
142 lines
2.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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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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"(1797, 64)\n"
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]
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}
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],
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"source": [
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"from sklearn import datasets\n",
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"digits = datasets.load_digits()\n",
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"print(digits.data.shape)"
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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": 2,
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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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"(1437, 64)\n",
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"(1437,)\n"
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]
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}
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],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.2, random_state=0)\n",
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"print(X_train.shape)\n",
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"print(y_train.shape)"
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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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{
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"data": {
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"text/plain": [
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"0.82499999999999996"
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]
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},
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"execution_count": 3,
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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.naive_bayes import GaussianNB\n",
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"nb = GaussianNB()\n",
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"nb.fit(X_train, y_train)\n",
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"nb.score(X_test,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": 4,
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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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"0.85833333333333328"
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]
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},
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"execution_count": 4,
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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.tree import DecisionTreeClassifier\n",
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"dt = DecisionTreeClassifier()\n",
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"dt.fit(X_train, y_train)\n",
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"dt.score(X_test,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": 5,
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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 0.95 confidence interval: 0.81 (+/- 0.11)\n"
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]
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}
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],
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"source": [
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"from sklearn.naive_bayes import GaussianNB\n",
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"from sklearn.model_selection import cross_val_score\n",
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"nb2 = GaussianNB()\n",
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"scores = cross_val_score(nb2, digits.data, digits.target, cv=10)\n",
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"print(\"Accuracy 0.95 confidence interval: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
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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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"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": "3.7.1"
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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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