X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
import pandas as pd from sklearn.datasets import load_iris iris = load_iris() df = pd.DataFrame(iris.data, columns=iris.feature_names) df['target'] = iris.target
model = DecisionTreeClassifier() model.fit(X_train, y_train)
pip install pandas numpy matplotlib scikit-learn We’ll use a built‑in dataset: Iris flowers.
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
import pandas as pd from sklearn.datasets import load_iris iris = load_iris() df = pd.DataFrame(iris.data, columns=iris.feature_names) df['target'] = iris.target datamine tutorial
model = DecisionTreeClassifier() model.fit(X_train, y_train) y_test = train_test_split(X
pip install pandas numpy matplotlib scikit-learn We’ll use a built‑in dataset: Iris flowers. datamine tutorial