Add Decision Tree Classifier with overfitting controls

This script implements a Decision Tree Classifier with controls to reduce overfitting on the Iris dataset. It includes model training, predictions, and accuracy evaluation.

Author: ruheena-shaik

reduce overfit.py

@@ -0,0 +1,31 @@
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score
+
+# Load dataset
+data = load_iris()
+X, y = data.data, data.target
+
+# Train-test split
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.3, random_state=42)
+
+# Decision Tree with Overfitting Controls
+model = DecisionTreeClassifier(
+    max_depth=3,              # limit depth of tree
+    min_samples_split=4,      # minimum samples to split a node
+    min_samples_leaf=2,       # minimum samples in each leaf
+    ccp_alpha=0.01,           # pruning parameter (cost-complexity pruning)
+    random_state=42
+)
+
+model.fit(X_train, y_train)
+
+# Predictions
+y_pred = model.predict(X_test)
+
+# Accuracy
+print("Accuracy:", accuracy_score(y_test, y_pred))
+print("Depth of Tree:", model.get_depth())
+print("Number of Leaves:", model.get_n_leaves())