Decision Tree for Regression

These flashcards cover key concepts related to decision tree regressors, including metrics for evaluating performance.

Decision tree regressor

A decision tree used for regression tasks that divides input feature space using inequalities.

Leaf

A terminal node in a decision tree where the predicted value is computed from training instances.

Mean Squared Error (MSE)

A common measure of the average squared difference between predicted and actual values.

Mean Absolute Error (MAE)

A metric that measures the average magnitude of errors between predicted and actual values, without considering their direction.

Friedman's Improvement (FI)

A measure used to evaluate the performance of a decision tree regression model, comparing the differences in predictions.

Cost Complexity Pruning

A technique used in decision trees to remove nodes that do not significantly improve the model's performance.

Error estimate

An assessment of the accuracy of a model based on its predictions compared to actual values.

Performance evaluation

The process of assessing how well a decision tree regressor performs using metrics like MSE and MAE.

Subtree

A section of a decision tree that can be analyzed separately.

Cost complexity formula

Rx(T) = R(T) + alpha * |T|, where R(T) is the error estimate of the tree and |T| is the number of leaves.