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More Classifiers

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raygaeta 2 years ago
parent f6855e0c65
commit 4873a37d14
1 changed files with 481 additions and 17 deletions
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4-Classification/3-Classifiers-2/notebook.ipynb
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@ -9,12 +9,179 @@
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@ -53,11 +220,10 @@
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]
},
"execution_count": 40,
"metadata": {},
"execution_count": 11
"output_type": "execute_result"
}
],
"source": [
"cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
"cuisines_feature_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import SVC\n",
"from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
"from sklearn.model_selection import train_test_split, cross_val_score\n",
"from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"C = 10\n",
"# Create different classifiers.\n",
"classifiers = {\n",
" 'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0),\n",
" 'KNN classifier': KNeighborsClassifier(C),\n",
" 'SVC': SVC(),\n",
" 'RFST': RandomForestClassifier(n_estimators=100),\n",
" 'ADA': AdaBoostClassifier(n_estimators=100)\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy (train) for Linear SVC: 78.6% \n",
" precision recall f1-score support\n",
"\n",
" chinese 0.66 0.72 0.69 233\n",
" indian 0.88 0.86 0.87 236\n",
" japanese 0.80 0.73 0.76 250\n",
" korean 0.82 0.75 0.78 248\n",
" thai 0.79 0.87 0.82 232\n",
"\n",
" accuracy 0.79 1199\n",
" macro avg 0.79 0.79 0.79 1199\n",
"weighted avg 0.79 0.79 0.79 1199\n",
"\n",
"Accuracy (train) for KNN classifier: 73.0% \n",
" precision recall f1-score support\n",
"\n",
" chinese 0.67 0.69 0.68 233\n",
" indian 0.81 0.80 0.80 236\n",
" japanese 0.66 0.84 0.74 250\n",
" korean 0.92 0.53 0.68 248\n",
" thai 0.70 0.80 0.75 232\n",
"\n",
" accuracy 0.73 1199\n",
" macro avg 0.75 0.73 0.73 1199\n",
"weighted avg 0.75 0.73 0.73 1199\n",
"\n",
"Accuracy (train) for SVC: 81.9% \n",
" precision recall f1-score support\n",
"\n",
" chinese 0.73 0.75 0.74 233\n",
" indian 0.90 0.88 0.89 236\n",
" japanese 0.84 0.79 0.81 250\n",
" korean 0.87 0.79 0.83 248\n",
" thai 0.78 0.89 0.83 232\n",
"\n",
" accuracy 0.82 1199\n",
" macro avg 0.82 0.82 0.82 1199\n",
"weighted avg 0.82 0.82 0.82 1199\n",
"\n",
"Accuracy (train) for RFST: 84.8% \n",
" precision recall f1-score support\n",
"\n",
" chinese 0.77 0.79 0.78 233\n",
" indian 0.90 0.92 0.91 236\n",
" japanese 0.89 0.80 0.84 250\n",
" korean 0.87 0.82 0.84 248\n",
" thai 0.81 0.91 0.86 232\n",
"\n",
" accuracy 0.85 1199\n",
" macro avg 0.85 0.85 0.85 1199\n",
"weighted avg 0.85 0.85 0.85 1199\n",
"\n",
"Accuracy (train) for ADA: 69.9% \n",
" precision recall f1-score support\n",
"\n",
" chinese 0.62 0.48 0.54 233\n",
" indian 0.84 0.84 0.84 236\n",
" japanese 0.69 0.57 0.62 250\n",
" korean 0.66 0.81 0.73 248\n",
" thai 0.68 0.79 0.73 232\n",
"\n",
" accuracy 0.70 1199\n",
" macro avg 0.70 0.70 0.69 1199\n",
"weighted avg 0.70 0.70 0.69 1199\n",
"\n"
]
}
],
"source": [
"n_classifiers = len(classifiers)\n",
"\n",
"for index, (name, classifier) in enumerate(classifiers.items()):\n",
" classifier.fit(X_train, np.ravel(y_train))\n",
"\n",
" y_pred = classifier.predict(X_test)\n",
" accuracy = accuracy_score(y_test, y_pred)\n",
" print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n",
" print(classification_report(y_test,y_pred))"
]
}
],
"metadata": {
@ -126,8 +590,8 @@
"hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3.7.0 64-bit ('3.7')"
"display_name": "Python 3.7.0 64-bit ('3.7')",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
@ -139,7 +603,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
"version": "3.12.0"
},
"metadata": {
"interpreter": {
@ -149,4 +613,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
}
}

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