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ML-For-Beginners/translations/sk/4-Classification/3-Classifiers-2/solution/notebook.ipynb

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{
"cells": [
{
"source": [
"# Vytvorte viac modelov klasifikácie\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prehľad datasetu\n",
"Tento dataset obsahuje jednotlivé vzorky (napríklad recepty) označené podľa kuchyne.\n",
"Každý riadok zodpovedá jednej vzorke/záznamu a stĺpce predstavujú ingrediencie alebo iné atribúty použité na klasifikáciu, vrátane označenia `cuisine`.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"# Load dataset containing cuisine features\n",
"cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
"cuisines_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 indian\n",
"1 indian\n",
"2 indian\n",
"3 indian\n",
"4 indian\n",
"Name: cuisine, dtype: object"
]
},
"metadata": {},
"execution_count": 2
}
],
"source": [
"cuisines_label_df = cuisines_df['cuisine']\n",
"cuisines_label_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" almond angelica anise anise_seed apple apple_brandy apricot \\\n",
"0 0 0 0 0 0 0 0 \n",
"1 1 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 0 \n",
"\n",
" armagnac artemisia artichoke ... whiskey white_bread white_wine \\\n",
"0 0 0 0 ... 0 0 0 \n",
"1 0 0 0 ... 0 0 0 \n",
"2 0 0 0 ... 0 0 0 \n",
"3 0 0 0 ... 0 0 0 \n",
"4 0 0 0 ... 0 0 0 \n",
"\n",
" whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n",
"0 0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 1 0 \n",
"\n",
"[5 rows x 380 columns]"
],
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>almond</th>\n <th>angelica</th>\n <th>anise</th>\n <th>anise_seed</th>\n <th>apple</th>\n <th>apple_brandy</th>\n <th>apricot</th>\n <th>armagnac</th>\n <th>artemisia</th>\n <th>artichoke</th>\n <th>...</th>\n <th>whiskey</th>\n <th>white_bread</th>\n <th>white_wine</th>\n <th>whole_grain_wheat_flour</th>\n <th>wine</th>\n <th>wood</th>\n <th>yam</th>\n <th>yeast</th>\n <th>yogurt</th>\n <th>zucchini</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
},
"metadata": {},
"execution_count": 3
}
],
"source": [
"cuisines_features_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
"cuisines_features_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Vyskúšajte rôzne klasifikátory\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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": 5,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(cuisines_features_df, cuisines_label_df, test_size=0.3)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"\n",
"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",
" \n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Accuracy (train) for Linear SVC: 76.4% \n",
" precision recall f1-score support\n",
"\n",
" chinese 0.64 0.66 0.65 242\n",
" indian 0.91 0.86 0.89 236\n",
" japanese 0.72 0.73 0.73 245\n",
" korean 0.83 0.75 0.79 234\n",
" thai 0.75 0.82 0.78 242\n",
"\n",
" accuracy 0.76 1199\n",
" macro avg 0.77 0.76 0.77 1199\n",
"weighted avg 0.77 0.76 0.77 1199\n",
"\n",
"Accuracy (train) for KNN classifier: 70.7% \n",
" precision recall f1-score support\n",
"\n",
" chinese 0.65 0.63 0.64 242\n",
" indian 0.84 0.81 0.82 236\n",
" japanese 0.60 0.81 0.69 245\n",
" korean 0.89 0.53 0.67 234\n",
" thai 0.69 0.75 0.72 242\n",
"\n",
" accuracy 0.71 1199\n",
" macro avg 0.73 0.71 0.71 1199\n",
"weighted avg 0.73 0.71 0.71 1199\n",
"\n",
"Accuracy (train) for SVC: 80.1% \n",
" precision recall f1-score support\n",
"\n",
" chinese 0.71 0.69 0.70 242\n",
" indian 0.92 0.92 0.92 236\n",
" japanese 0.77 0.78 0.77 245\n",
" korean 0.87 0.77 0.82 234\n",
" thai 0.75 0.86 0.80 242\n",
"\n",
" accuracy 0.80 1199\n",
" macro avg 0.80 0.80 0.80 1199\n",
"weighted avg 0.80 0.80 0.80 1199\n",
"\n",
"Accuracy (train) for RFST: 82.8% \n",
" precision recall f1-score support\n",
"\n",
" chinese 0.80 0.75 0.77 242\n",
" indian 0.90 0.91 0.90 236\n",
" japanese 0.82 0.78 0.80 245\n",
" korean 0.85 0.82 0.83 234\n",
" thai 0.78 0.89 0.83 242\n",
"\n",
" accuracy 0.83 1199\n",
" macro avg 0.83 0.83 0.83 1199\n",
"weighted avg 0.83 0.83 0.83 1199\n",
"\n",
"Accuracy (train) for ADA: 71.1% \n",
" precision recall f1-score support\n",
"\n",
" chinese 0.60 0.57 0.58 242\n",
" indian 0.87 0.84 0.86 236\n",
" japanese 0.71 0.60 0.65 245\n",
" korean 0.68 0.78 0.72 234\n",
" thai 0.70 0.78 0.74 242\n",
"\n",
" accuracy 0.71 1199\n",
" macro avg 0.71 0.71 0.71 1199\n",
"weighted avg 0.71 0.71 0.71 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))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Upozornenie**: \nTento dokument bol preložený pomocou AI prekladateľskej služby [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, majte prosím na pamäti, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Originálny dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nie sme zodpovední za akékoľvek nedorozumenia alebo nesprávne interpretácie vzniknuté použitím tohto prekladu.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
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