Regularization Techniques for Decision Trees - Python Automation and Machine Learning for ICs - - An Online Book - |
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================================================================================= Some regulization techniques can be used to control the complexity of decision trees and prevent overfitting. In machine learning, regularization refers to methods that help prevent a model from fitting the training data too closely, which can lead to poor generalization on new, unseen data. While pruning is the traditional technique for reducing overfitting in decision trees, some regularization techniques can also be applied to avoid overfitting. Regularization techniques such as limiting the depth of the tree, controlling the minimum number of samples required to split a node, or setting a minimum impurity decrease for splitting a node can all help prevent overfitting in decision trees. These techniques effectively restrict the complexity of the tree, making it less likely to fit noise in the training data and improving its generalization performance on unseen data. Additionally, ensemble methods such as bagging and boosting can also be considered regularization techniques in a broader sense. Bagging (Bootstrap Aggregating) involves training multiple decision trees on different subsets of the training data and combining their predictions, which helps to reduce variance and overfitting. Boosting algorithms, such as AdaBoost and Gradient Boosting, sequentially train decision trees, with each subsequent tree focusing on correcting the errors of the previous ones. This iterative process can also help prevent overfitting by emphasizing the training instances that are more difficult to classify. In summary, some regularization techniques that can be applied to decision trees are:
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