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Is random forest robust to imbalanced data?
Like bagging, random forest involves selecting bootstrap samples from the training dataset and fitting a decision tree on each. Again, random forest is very effective on a wide range of problems, but like bagging, performance of the standard algorithm is not great on imbalanced classification problems.
Is random forest robust to class imbalance?
The random forest model is built on decision trees, and decision trees are sensitive to class imbalance. Each tree is built on a “bag”, and each bag is a uniform random sample from the data (with replacement). Therefore each tree will be biased in the same direction and magnitude (on average) by class imbalance.
Do you use an imbalanced sample in logistic regression?
Having said which, 4:1 is not all that imbalanced. If you use a balanced dataset, the important thing is to remember that the output of the model is now an estimate of the a-posteriori probability, assuming the classes are equally common, and so you may end up biasing the model too far.
How does a weighted random forest model work?
Weighted Random Forest, as a variant of random forest, consists of multiple weighted decision trees. The final output by the weighted random forest is the class that have the majority votes from individual weighted decision trees. Now, let’s see how it works on the credit card fraud detection problem:
Which is better weighted decision tree or logistic regression classifier?
One important point should be pointed out: the weighted decision tree classifier has a more ‘balanced’ performance (pun intended!) on precision and recall. By assigning a much heavier weight on positive class, recall is improved by 10% compared to logistic regression classifier at the expense of precision.
Which is the best random forest for imbalanced data?
Balanced Random Forest did really badly in classifying the majority class samples (the normal transactions). One advantage gained by sacrificing precision: its recall score is the highest among the classifiers presented in this post. For extremely imbalanced datasets, each bootstrap contains few or even none of the minority class samples.