Key Resampling Techniques Used in Machine Learning
The field of machine learning relies heavily on resampling methods in machine learning to evaluate model performance and improve reliability. Techniques such as cross-validation techniques help ensure that models generalize well by testing them on different subsets of data. By repeatedly training and validating models on varied samples, resampling strategies reduce overfitting, enhance accuracy, and provide more trustworthy performance estimates for real-world applications.