Fisher Score

The Fisher Score is a method used in feature selection to identify which variables are most useful for distinguishing between different groups or categories in data. It measures how well a feature separates classes by comparing the difference in average values between groups to the variability within each group. A high Fisher Score indicates that the feature has a clear distinction across classes with minimal overlap, making it valuable for classification tasks. Essentially, it helps select features that best discriminate between categories, improving the performance of machine learning models.