Decision Trees (Supervised Learning)

Short summary

Decision Trees automatically discover if-else rules by recursively splitting data into purer groups, guided by Gini impurity and information gain metrics. They excel at non-linear predictions, handle mixed data types, and offer interpretability without preprocessing—but risk overfitting unless regularization limits tree depth. Production tip: shallow trees with regularization parameters (max_depth, min_samples_split) generalize better than deep trees memorizing training data.

• •Decision Trees split data using simple conditional rules, choosing splits that maximize purity (Gini impurity) and information gain
• •Benefits: handles non-linear relationships, mixed data types, no feature scaling, highly interpretable; weaknesses: prone to overfitting, unstable, greedy decisions
• •Control tree growth with max_depth, min_samples_split to prevent memorizing training data

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