- Tree Pruning is performed in order to remove anomalies in training data due to noise or outliers.
- The pruned trees are smaller and less complex.
- Pruning is a technique in machine learning that reduces the size of decision trees by removing sections of the tree that provide little power to classify instances.
- The dual goal of pruning is reduced complexity of the final classifier as well as better predictive accuracy by the reduction of overfitting and removal of sections of a classifier that may be based on noisy or erroneous data.
- One of the questions that arise in a decision tree algorithm is the optimal size of the final tree.
- A tree that is too large risks overfitting the training data and poorly generalizing to new samples.
- A small tree might not capture important structural information about the sample space.
- It is hard to tell when a tree algorithm should stop because it is impossible to tell if the addition of a single extra node will dramatically decrease error.
- A common strategy is to grow the tree until each node contains a small number of instances then use pruning to remove nodes that do not provide additional information.
- Pruning should reduce the size of a learning tree without reducing predictive accuracy as measured by a test set or using cross-validation.
- There are many techniques for tree pruning that differ in the measurement that is used to optimize performance.
Tree pruning approaches
- Prepruning - The tree is pruned by halting its construction early.
- Postpruning - This approach removes subtree form fully grown tree.
- Based on statistical significance test.
- Stop growing the tree when there is no statistically significant association between any attribute and the class at a particular node
- Most popular test: chi-squared test
- ID3 used chi-squared test in addition to information gain.
- Only statistically significant attributes were allowed to be selected by information gain procedure.
- Pre-pruning may stop the growth process prematurely: early stopping
- Pre-pruning faster than post-pruning
- First, build full tree then, prune it.
- Fully-grown tree shows all attribute interactions
- Problem: some subtrees might be due to chance effects
- Two pruning operations:
- Subtree replacement
- Subtree raising
- error estimation
- significance testing
- MDL principle
- Subtree replacement selects a subtree and replaces it with a single leaf.
- Subtree raising selects a subtree and replaces it with the child one ie, a "sub-subtree" replaces its parent)