How do you tune the pruning parameters in c4.5 algorithm to avoid overfitting or underfitting?

1 Pruning in c4.5

Pruning is a technique that reduces the size of the decision tree by removing branches or nodes that do not contribute much to the prediction performance. This can improve the model's simplicity, speed, and robustness. C4.5 algorithm uses two types of pruning: pre-pruning and post-pruning. Pre-pruning stops the tree growth before it reaches the maximum depth or minimum number of instances per leaf, based on some criteria such as information gain or confidence level. Post-pruning cuts off some branches or nodes after the tree is fully grown, based on a validation set or a statistical test.

2 Tuning pre-pruning parameters

To tune the pre-pruning parameters in c4.5 algorithm, you need to specify the minimum number of instances per leaf (m) and the confidence level for pruning (c). The m parameter determines how many training examples are required for each leaf node, and the c parameter determines how much the expected error rate can deviate from the observed error rate before pruning. A lower m value allows more splits and a larger tree, which can lead to overfitting. A higher m value reduces the number of splits and a smaller tree, which can lead to underfitting. A lower c value means that the algorithm is more conservative and prunes more aggressively, which can also lead to underfitting. A higher c value means that the algorithm is more optimistic and prunes less, which can lead to overfitting.

3 Tuning post-pruning parameters

To tune the post-pruning parameters in c4.5 algorithm, you need to specify the method and the criteria for pruning. The method can be either reduced error pruning (REP) or pessimistic error pruning (PEP). REP uses a validation set to evaluate the error rate of each subtree and prunes it if it does not improve the performance. PEP uses a statistical test to estimate the upper bound of the error rate of each subtree and prunes it if it exceeds a threshold. The criteria for pruning can be either accuracy or complexity. Accuracy means that the algorithm tries to maximize the prediction accuracy on the validation set or the test set. Complexity means that the algorithm tries to minimize the size of the tree or the number of nodes.

4 Finding the optimal parameters

Finding the optimal pruning parameters in c4.5 algorithm is not a trivial task, as there is no single best combination that works for all datasets and situations. You need to experiment with different values and methods and compare their results using some metrics such as accuracy, precision, recall, F1-score, or AUC. You can also use cross-validation or grid search to find the best parameters systematically. However, you should be aware of the trade-offs and limitations of each approach and avoid overfitting or underfitting the pruning process itself.

5 Evaluating the pruned tree

After you tune the pruning parameters in c4.5 algorithm, you should evaluate the performance of the pruned tree on the test set or a new dataset. You should check if the pruned tree has improved the generalization ability and reduced the variance or bias of the model. You should also inspect the structure and the rules of the pruned tree and see if they make sense and are interpretable. You can use some visualization tools or techniques to display the pruned tree and its attributes.

6 Applying the pruned tree

Finally, you can apply the pruned tree to make predictions or classifications on new data. You should use the same preprocessing and encoding steps that you used for the training data. You should also monitor the performance and the accuracy of the pruned tree over time and update it if necessary. You can also use the pruned tree as a baseline or a feature for other machine learning models or algorithms.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?