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Merge branch 'main' of https://gitea.jany.se/Jany/MLPproject

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jany
2025-10-22 12:34:23 +02:00
parent 342dfe7e1f 87901ed254
commit af4291fe1e
3 changed files with 103 additions and 249 deletions
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Decision_tree.ipynb
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@@ -8,7 +8,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 24,
"id": "0952f099",
"metadata": {},
"outputs": [
@@ -45,7 +45,26 @@
" accuracy 0.81 6033\n",
" macro avg 0.74 0.75 0.74 6033\n",
"weighted avg 0.81 0.81 0.81 6033\n",
"\n"
"\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 18096 entries, 13586 to 5836\n",
"Data columns (total 12 columns):\n",
" # Column Non-Null Count Dtype\n",
"--- ------ -------------- -----\n",
" 0 age 18096 non-null int64\n",
" 1 workclass 18096 non-null int64\n",
" 2 education.num 18096 non-null int64\n",
" 3 marital.status 18096 non-null int64\n",
" 4 occupation 18096 non-null int64\n",
" 5 relationship 18096 non-null int64\n",
" 6 race 18096 non-null int64\n",
" 7 sex 18096 non-null int64\n",
" 8 capital.gain 18096 non-null int64\n",
" 9 capital.loss 18096 non-null int64\n",
" 10 hours.per.week 18096 non-null int64\n",
" 11 native.country 18096 non-null int64\n",
"dtypes: int64(12)\n",
"memory usage: 1.8 MB\n"
]
}
],
@@ -53,12 +72,14 @@
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from sklearn.metrics import mean_squared_error, mean_absolute_error , r2_score\n",
"from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
"from scipy.stats import randint\n",
"\n",
"\n",
"# Load dataset\n",
"df = pd.read_csv('./Datasets/adult.csv', comment = '#')\n",
@@ -123,16 +144,63 @@
"plt.show() \n",
"\n",
"print(\"Classification Report:\")\n",
"print(classification_report(y_val, y_pred, target_names=[\"Poor\", \"Rich\"]))"
"print(classification_report(y_val, y_pred, target_names=[\"Poor\", \"Rich\"]))\n",
"\n",
"X_train.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 26,
"id": "e567e4e9",
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" Poor 0.89 0.92 0.90 4524\n",
" Rich 0.73 0.65 0.68 1509\n",
"\n",
" accuracy 0.85 6033\n",
" macro avg 0.81 0.78 0.79 6033\n",
"weighted avg 0.85 0.85 0.85 6033\n",
"\n"
]
}
],
"source": [
"# Hyperparameters search space\n",
"param_dist = {\n",
" 'full_dt_classifier__max_depth': randint(3, 20),\n",
" 'full_dt_classifier__min_samples_split': randint(2, 10),\n",
" 'full_dt_classifier__min_samples_leaf': randint(1, 10),\n",
"}\n",
"\n",
"# Ranodmized search for hyperparameter tuning\n",
"random_search = RandomizedSearchCV(\n",
" estimator=model,\n",
" param_distributions=param_dist,\n",
" n_iter = 50,\n",
" cv = 10,\n",
" scoring = 'r2',\n",
" n_jobs = -1,\n",
" random_state = 42\n",
")\n",
"\n",
"# Fit search\n",
"random_search.fit(X_train, y_train)\n",
"\n",
"# Best model training\n",
"best_model = random_search.best_estimator_\n",
"y_pred_best = best_model.predict(X_val)\n",
"\n",
"print(\"Classification Report:\")\n",
"print(classification_report(y_val, y_pred_best, target_names=[\"Poor\", \"Rich\"]))"
]
}
],
"metadata": {