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Decision_trees #3

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s481838 merged 3 commits from Decision_trees into master 2024-05-20 23:11:52 +02:00
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source/decision_tree.py
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@ -23,8 +23,6 @@ X = data.drop('target_column', axis=1)
y = data['target_column'] y = data['target_column']
X = pd.get_dummies(X) X = pd.get_dummies(X)
print(X)
print(y)
# Podział danych na zbiór treningowy i testowy # Podział danych na zbiór treningowy i testowy
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
@ -33,7 +31,7 @@ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_
model = DecisionTreeClassifier() model = DecisionTreeClassifier()
model.fit(X_train, y_train) model.fit(X_train, y_train)
fig = plt.figure(figsize=(25,20)) fig = plt.figure(figsize=(25, 20))
_ = plot_tree(model, feature_names=X.columns, class_names=model.classes_, filled=True) _ = plot_tree(model, feature_names=X.columns, class_names=model.classes_, filled=True)
plt.savefig('drzewo_decyzyjne.png') plt.savefig('drzewo_decyzyjne.png')
plt.show() plt.show()