The correct answer is C. Both by pruning the longer rules and by creating new rules.
Overfitting is a common problem in machine learning, and decision trees are no exception. Overfitting occurs when a model learns the training data too well, and as a result, it does not generalize well to new data. This can lead to poor performance on unseen data.
There are a number of ways to counter overfitting in decision trees. One common approach is to prune the tree. This involves removing some of the branches from the tree, which can help to reduce the model’s complexity and improve its generalization performance.
Another approach is to create new rules. This involves adding new branches to the tree, which can help the model to capture more complex relationships between the features and the target variable.
In some cases, it may be necessary to use both pruning and rule creation to counter overfitting. The best approach will depend on the specific data set and the desired model performance.
Here is a more detailed explanation of each option:
- Option A: By pruning the longer rules. This is a common approach to counter overfitting in decision trees. Pruning involves removing some of the branches from the tree, which can help to reduce the model’s complexity and improve its generalization performance.
- Option B: By creating new rules. This is another common approach to counter overfitting in decision trees. Creating new rules involves adding new branches to the tree, which can help the model to capture more complex relationships between the features and the target variable.
- Option C: Both by pruning the longer rules and by creating new rules. This is the most comprehensive approach to counter overfitting in decision trees. It involves using both pruning and rule creation to reduce the model’s complexity and improve its generalization performance.
- Option D: Over-fitting is not possible. This is not a correct option. Overfitting is a common problem in machine learning, and decision trees are no exception.