Problems with the imbalanced data

Problems with the imbalanced data

Unbalanced class distributions present a barrier, even though many machine learning algorithms work best when there are nearly equal numbers of samples in each class. A model may appear to have high accuracy in these situations if it primarily predicts the majority class. In such cases, having high accuracy becomes deceptive. Sadly, the minority class—which is frequently the main focus of model creation—is ignored by this strategy. In the event that 99% of the data pertains to the majority class, for example, simple classification models such as logistic regression or decision trees may find it difficult to recognize and precisely forecast occurrences from the minority class....

Class Imbalance Handling in Machine Learning

Resampling, which modifies the sample distribution, is a frequently used technique for handling very unbalanced datasets. This can be accomplished by either over-sampling, which adds more examples from the minority class, or under-sampling, which removes samples from the majority class. One method for reducing the difficulties caused by severely skewed datasets is resampling, which balances the class distribution....

Balancing data with the Imbalanced-Learn module in Python

In the world of fixing imbalanced data, there are some smart tricks. Scientists have come up with advanced methods to handle this issue....