Feature	ID3	C4.5	CART
Developer	Ross Quinlan	Ross Quinlan	Breiman et al.
Splitting Criterion	Information Gain	Gain Ratio	Gini Impurity (Classification), Mean Squared Error (Regression)
Data Types Supported	Categorical	Categorical & Continuous	Categorical & Continuous
Pruning	No	Post-pruning	Cost-complexity pruning
Output	Multi-way splits	Multi-way splits	Binary splits
Regression Support	No	No	Yes
Handles Missing Data	No	Yes	Yes
Computational Efficiency	Fast	Moderate	Moderate
Bias Towards Features	Yes, towards features with many categories	No, uses Gain Ratio to normalize	No
Strengths	Simple and easy to implement	Handles continuous data, avoids bias, post-pruning	Supports both classification and regression; robust
Weaknesses	Overfitting prone, categorical data only	Slower than ID3, no regression	Only binary splits, more computationally intensive
Applications	Simple classification tasks (e.g., weather prediction)	Complex classification tasks (e.g., disease diagnosis)	Both classification and regression (e.g., fraud detection, house price prediction)