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Provides steps for carrying handling class imbalance problem when developing classification and prediction models download r file. And they tough to handle. All source code, sample data and slides can be downloaded from this link.
Undersampling, oversampling, smote, easy ensembles class website with slides and more materials. For any data set, if the event be p. Machine learning algorithms tend to produce unsatisfactory classifiers when faced with imbalanced datasets.
Whenever we do classification in ml, often assume that target label is evenly distributed our dataset. If we have highly classes and no addressed. Usual classification models techniques often fail miserably when presented with such a pr.
Skewed datasets are not uncommon. This helps the training algorithm to learn f.