Decision Tree Learning
The problem: given a data set, produce the shortest-depth decision tree that accurately classifies the data
The (heuristic): build the tree greedily on the basis of expected entropy loss
Common problems
- the training set is not a good surrogate for the full data set
- noise
- spurious correlations
- thus the optimal tree for the test set may not be accurate for the full data set (overfitting)
- missing values in training set or subsequent cases