How do Decision Trees handle imbalanced data?
Decision trees frequently perform well on imbalanced data. They work by learning a hierarchy of if/else questions and this can force both classes to be addressed. While our accuracy score is slightly lower, both F1 and recall have increased as compared to logistic regression!
Does Decision Tree need balanced data?
The decision tree algorithm is effective for balanced classification, although it does not perform well on imbalanced datasets. In this tutorial, you will discover the weighted decision tree for imbalanced classification.
How is class weight used in a decision tree?
The DecisionTreeClassifier class provides the class_weight argument that can be specified as a model hyperparameter. The class_weight is a dictionary that defines each class label (e.g. 0 and 1) and the weighting to apply in the calculation of group purity for splits in the decision tree when fitting the model.
How to train an unbalanced decision tree classifier?
A common practice for unbalanced data is to give higher weight to the samples of the small classes, e.g., we can weight each sample by the inverse class frequency. You can train the decision tree classifier using the method described on this answer: http://www.csresearchers.com/?qa=12/using-sample-weighting-with-svm-decision-tree-classifiers
How to calculate class purity in a decision tree?
The class_weight is a dictionary that defines each class label (e.g. 0 and 1) and the weighting to apply in the calculation of group purity for splits in the decision tree when fitting the model. For example, a 1 to 1 weighting for each class 0 and 1 can be defined as follows: The class weighing can be defined multiple ways; for example:
How to create a weighted decision tree in Python?
Weighted Decision Tree With Scikit-Learn The scikit-learn Python machine learning library provides an implementation of the decision tree algorithm that supports class weighting. The DecisionTreeClassifier class provides the class_weight argument that can be specified as a model hyperparameter.