Usuwanie obserwacji odstających
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"### Metryki dla zadań regresji\n",
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"\n",
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"Dla zadań regresji możemy zastosować np.:\n",
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" * błąd średniokwadratowy (*root-mean-square error*, RMSE):\n",
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" * błąd średniokwadratowy (*mean-square error*, MSE):\n",
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" $$ \\mathrm{MSE} \\, = \\, \\frac{1}{m} \\sum_{i=1}^{m} \\left( \\hat{y}^{(i)} - y^{(i)} \\right)^2 $$\n",
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" * pierwiastek z błędu średniokwadratowego (*root-mean-square error*, RMSE):\n",
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" $$ \\mathrm{RMSE} \\, = \\, \\sqrt{ \\frac{1}{m} \\sum_{i=1}^{m} \\left( \\hat{y}^{(i)} - y^{(i)} \\right)^2 } $$\n",
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" * średni błąd bezwzględny (*mean absolute error*, MAE):\n",
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" $$ \\mathrm{MAE} \\, = \\, \\frac{1}{m} \\sum_{i=1}^{m} \\left| \\hat{y}^{(i)} - y^{(i)} \\right| $$"
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"cell_type": "code",
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"execution_count": 24,
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 12,
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "32929ab5e3024128bd39a6c165e50196",
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"model_id": "6325cec10a034a9d96d862dee900013d",
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"version_major": 2,
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"version_minor": 0
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"<function __main__.interactive_classification(highlight)>"
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]
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},
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"execution_count": 35,
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -1185,9 +1187,10 @@
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"# Odrzućmy obserwacje odstające\n",
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"alldata_no_outliers = [\n",
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" (index, item) for index, item in alldata.iterrows() \n",
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" if item.price > 100 and item.sqrMetres > 10]\n",
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" if item.price > 10000 and item.sqrMetres < 1000]\n",
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"\n",
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"alldata_no_outliers = alldata.loc[(alldata['price'] > 100) & (alldata['sqrMetres'] > 100)]"
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"# Alternatywnie można to zrobić w następujący sposób\n",
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"alldata_no_outliers = alldata.loc[(alldata['price'] > 10000) & (alldata['sqrMetres'] < 1000)]"
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]
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},
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{
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