Statistical Techniques

Bootstrap aggregating, or bagging, is a machine learning ensemble technique that improves model accuracy by combining multiple models.

Frequentist statistics focuses on the frequency of events to draw conclusions about populations from sample data.

K-Fold Cross Validation is a technique for assessing the performance of machine learning models using multiple data subsets.

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

Parametric statistics rely on assumptions about data distribution for inference and hypothesis testing.