{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "WN_Wk5YLEqjH"
},
"source": [
"Trenowanie i sprawdzanie modelu na podstawie danych\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
},
"id": "m_clYNei0tkC",
"outputId": "33bac564-a242-4c57-9c97-5bd309f94f67"
},
"outputs": [
{
"data": {
"text/html": [
"LinearRegression() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import mean_squared_error\n",
" \n",
"y_pred = model.predict(X_normalized)\n",
"mse = mean_squared_error(y, y_pred)\n",
"print(f\"Średni Błąd Kwadratowy (MSE): {mse:.2f}\")\n",
"\n",
"from sklearn.metrics import mean_absolute_error\n",
" \n",
"mae = mean_absolute_error(y, y_pred)\n",
"print(f\"Średni Błąd Bezwzględny (MAE): {mae:.2f}\")\n",
"\n",
"from sklearn.metrics import r2_score\n",
" \n",
"r2 = r2_score(y, y_pred)\n",
"print(f\"R-kwadrat (R2): {r2:.2f}\")\n",
"\n",
"import matplotlib.pyplot as plt\n",
" \n",
"plt.scatter(y, y_pred, label=\"Dane\")\n",
"\n",
"plt.xlabel(\"Rzeczywiste wartości\")\n",
"plt.ylabel(\"Przewidywane wartości\")\n",
"plt.title(\"Wykres predykcji vs. rzeczywistość z linią regresji\")\n",
"plt.legend()\n",
"plt.show()"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}