> ML_ALGORITHM // DECISION-TREES_v1.0
CART Decision Trees
Non-parametric supervised algorithm that recursively partitions the feature space into axis-aligned rectangular regions.
Tree & Rule Ensemblesclassical-supervisedhigh-intrinsicsmall (<1k)
Back to All AlgorithmsComputational Complexity
Training Complexity:O(p * n log n)
Inference Complexity:O(tree_depth)
Hardware Profile
CPU Friendly:Yes
Requires GPU:No
Memory Footprint:low
Interpretability & Data
Interpretability Tier:high-intrinsic
Training Data Needs:small (<1k)
Interpretability Assessment
Can be visually inspected as human-executable IF-THEN logic trees.
Suitable Tasks & Supported Modalities
Suitable Tasks:
binary classificationmulticlass classificationregression
Supported Modalities:
tabular
Implementing Libraries
Foundational Literature
Classification and Regression Trees (CART)Leo Breiman, Jerome H. Friedman (1984) · Wadsworth & Brooks/Cole Advanced Books & Software
Common Pitfalls & Warnings
- Growing unconstrained depth leading to severe overfitting
- High variance where small sample perturbations completely restructure the tree