Imbalanced Datasets

Borderline-SMOTE is an advanced technique for generating synthetic samples in imbalanced datasets, focusing on borderline instances.

Imbalanced data occurs when the classes in a dataset are not represented equally, often leading to biased model predictions.

Label imbalance refers to the unequal distribution of classes in a dataset used for training AI models.

The majority class refers to the category in a dataset that has the highest frequency of instances.

Oversampling minority class is a technique to balance imbalanced datasets by increasing the number of instances in the minority class.

SMOTE is a technique used to balance datasets by generating synthetic examples for underrepresented classes.