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Uaktualnienie lab. 5

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Paweł Skórzewski 2024-04-11 10:45:55 +02:00
parent 2af4ea2178
commit a367ed7abf
1 changed files with 12 additions and 27 deletions
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lab/05_Ewaluacja.ipynb
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@ -44,23 +44,16 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[332187.32537534]\n",
" [369587.77676738]\n",
" [488428.70420785]\n",
" [300013.00301966]\n",
" [412118.79730411]\n",
" [283333.7605634 ]\n",
" [275209.84706017]\n",
" [361970.50784352]\n",
" [272402.36116539]\n",
" [328635.55642844]]\n"
"[279661.8663101 279261.14658016 522543.09697553 243798.45172733\n",
" 408919.21577439 272940.5507781 367515.38801642 592972.56867895\n",
" 418509.89826131 943578.7139463 ]\n"
]
}
],
@ -84,7 +77,7 @@
"def preprocess(data):\n",
" \"\"\"Wstępne przetworzenie danych\"\"\"\n",
" data = data.replace({\"parter\": 0, \"poddasze\": 0}, regex=True)\n",
" data = data.applymap(np.nan_to_num) # Zamienia \"NaN\" na liczby\n",
" data = data.map(np.nan_to_num) # Zamienia \"NaN\" na liczby\n",
" return data\n",
"\n",
"\n",
@ -101,7 +94,7 @@
"data_train, data_test = train_test_split(data, test_size=0.2)\n",
"\n",
"# Uczenie modelu\n",
"y_train = pd.DataFrame(data_train[\"cena\"])\n",
"y_train = pd.Series(data_train[\"cena\"])\n",
"x_train = pd.DataFrame(data_train[FEATURES])\n",
"model = LinearRegression() # definicja modelu\n",
"model.fit(x_train, y_train) # dopasowanie modelu\n",
@ -154,14 +147,14 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Błąd średniokwadratowy wynosi 1179760250402.185\n"
"Błąd średniokwadratowy wynosi 137394744518.31197\n"
]
}
],
@ -182,14 +175,14 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-10.712011261173265\n"
"0.2160821272059249\n"
]
}
],
@ -213,7 +206,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 18,
"metadata": {},
"outputs": [
{
@ -225,14 +218,6 @@
"F-score: 1.0\n",
"Model score: 1.0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/pawel/.local/lib/python3.10/site-packages/sklearn/utils/validation.py:1111: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" y = column_or_1d(y, warn=True)\n"
]
}
],
"source": [
@ -254,7 +239,7 @@
"data_train, data_test = train_test_split(data_iris, test_size=0.2)\n",
"\n",
"# Uczenie modelu\n",
"y_train = pd.DataFrame(data_train[\"Iris setosa?\"])\n",
"y_train = pd.Series(data_train[\"Iris setosa?\"])\n",
"x_train = pd.DataFrame(data_train[FEATURES])\n",
"model = LogisticRegression() # definicja modelu\n",
"model.fit(x_train, y_train) # dopasowanie modelu\n",