Artificial intelligence · University
Decision trees, splits and ensembles · revision notes
A decision tree partitions feature space using a sequence of tests. Leaf predictions summarize the training examples reaching them.
Key ideas and conditions
Tree model
A decision tree partitions feature space using a sequence of tests. Leaf predictions summarize the training examples reaching them.
Impurity
For class probabilities pj, Gini impurity is 1−sum pj². A split is evaluated using the sizes and impurity of its child groups.
Complexity
Deep trees can fit small idiosyncratic groups. Depth limits, minimum leaf sizes and pruning can control complexity.
Ensembles
Combining trees can reduce some errors, but correlated mistakes and data defects can remain. Ensemble size does not replace proper evaluation.
Explain without looking
- Why can many models still share the same blind spot?
- Does a tree split establish a causal relationship?
Further reading and review references
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