ML Interview Question: How do you deal with class imbalance in a dataset?
Answers
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An effective way to handle imbalanced data is to downsample and upweight the majority class. Let's start by defining those two new terms: Downsampling (in this context) means training on a disproportionately low subset of the majority class examples.
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Class Imbalance is the problem where one class of targeted column is makes up the large proportions of the data in that case model just learns only one attribute and cannot perform well in the testing data and affects the accuracy of the model.
We can deal with problem of class imbalance by
1. Undersampling
2. Oversampling
3. Resampling
etc. -
Class imbalance is a problem that occurs in machine learning classification problems. It merely tells that the target class’s frequency is highly imbalanced, i.e., the occurrence of one of the classes is very high compared to the other classes present.
we can deal with class imbalance in a dataset by:
1.Under sampling (Downsampling)
2.Oversampling (Upsampling)
3. Resampling
etc.
