Kernel methods represent a cornerstone in modern machine learning, enabling algorithms to efficiently derive non-linear patterns by implicitly mapping data into high‐dimensional feature spaces.

The analysis highlights a mixed performance record for predictive models, with significant variability depending on the ...

Jun Shao, On Resampling Methods for Variance and Bias Estimation in Linear Models, The Annals of Statistics, Vol. 16, No. 3 (Sep., 1988), pp. 986-1008 ...

Our findings suggest that integrating machine learning into traditional statistical methods can provide more accurate and generalizable models for disease risk prediction.

Learn how to compare ML models using bootstrap resampling with a hands-on sklearn implementation.