Tour of Data Sampling Methods for Imbalanced Classification
Machine learning techniques often fail or give misleadingly optimistic performance on classification datasets with an imbalanced class distribution. The reason is that many machine learning algorithms are designed to operate on classification data with an equal number of observations for each class. When this is not the case, algorithms can learn that very few examples are not important and can be ignored in order to achieve good performance. Data sampling provides a collection of techniques that transform a training dataset […]
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