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algorithm
Decision Tree
beginner
supervised
non parametric
classification
regression
tabular
A Decision Tree predicts by following a sequence of feature-threshold questions to a leaf.
Mechanisms
recursive partitioning
threshold rules
Properties
interpretable
nonlinear
Constraints
poor extrapolation
sensitive to tuning
Practical profile
Explainability
high
Training cost
low
Inference cost
low
Data appetite
low
Concepts to understand
Bias and Variance
Generalization
Metrics
Regularization
Train, Validation, and Test Sets