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42
lab/04_Reprezentacja_danych.ipynb
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@ -28,7 +28,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -129,7 +129,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -176,7 +176,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -201,7 +201,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -229,7 +229,7 @@
"Name: Piętro, dtype: int64" "Name: Piętro, dtype: int64"
] ]
}, },
"execution_count": 4, "execution_count": 8,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -260,7 +260,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 18,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -285,7 +285,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 19,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -335,7 +335,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 20,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -352,7 +352,7 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"/tmp/ipykernel_1089/3804580172.py:1: RuntimeWarning: invalid value encountered in sqrt\n", "/tmp/ipykernel_11063/3804580172.py:1: RuntimeWarning: invalid value encountered in sqrt\n",
" print(np.sqrt(-1)) # niezdefiniowany wynik działania (pierwiastek z liczby ujemnej)\n" " print(np.sqrt(-1)) # niezdefiniowany wynik działania (pierwiastek z liczby ujemnej)\n"
] ]
} }
@ -387,30 +387,6 @@
"* [What’s the best way to handle NaN values?](https://towardsdatascience.com/whats-the-best-way-to-handle-nan-values-62d50f738fc)" "* [What’s the best way to handle NaN values?](https://towardsdatascience.com/whats-the-best-way-to-handle-nan-values-62d50f738fc)"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tak można policzyć, ile jest wartości NaN w danej kolumnie:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2983\n"
]
}
],
"source": [
"print(np.isnan(alldata[\"Rok budowy\"]).sum())"
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},

4
wyk/05_Metody_ewaluacji.ipynb
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@ -273,9 +273,7 @@
"### Metryki dla zadań regresji\n", "### Metryki dla zadań regresji\n",
"\n", "\n",
"Dla zadań regresji możemy zastosować np.:\n", "Dla zadań regresji możemy zastosować np.:\n",
" * błąd średniokwadratowy (*mean-square error*, MSE):\n", " * błąd średniokwadratowy (*root-mean-square error*, RMSE):\n",
" $$ \\mathrm{MSE} \\, = \\, \\frac{1}{m} \\sum_{i=1}^{m} \\left( \\hat{y}^{(i)} - y^{(i)} \\right)^2 $$\n",
" * pierwiastek z błędu średniokwadratowego (*root-mean-square error*, RMSE):\n",
" $$ \\mathrm{RMSE} \\, = \\, \\sqrt{ \\frac{1}{m} \\sum_{i=1}^{m} \\left( \\hat{y}^{(i)} - y^{(i)} \\right)^2 } $$\n", " $$ \\mathrm{RMSE} \\, = \\, \\sqrt{ \\frac{1}{m} \\sum_{i=1}^{m} \\left( \\hat{y}^{(i)} - y^{(i)} \\right)^2 } $$\n",
" * średni błąd bezwzględny (*mean absolute error*, MAE):\n", " * średni błąd bezwzględny (*mean absolute error*, MAE):\n",
" $$ \\mathrm{MAE} \\, = \\, \\frac{1}{m} \\sum_{i=1}^{m} \\left| \\hat{y}^{(i)} - y^{(i)} \\right| $$" " $$ \\mathrm{MAE} \\, = \\, \\frac{1}{m} \\sum_{i=1}^{m} \\left| \\hat{y}^{(i)} - y^{(i)} \\right| $$"