Kernel Ridge Regression (KRR) combines Ridge Regression with the kernel trick to enable efficient learning in high-dimensional feature spaces, allowing it to capture complex, non-linear relationships in the data. By incorporating a regularization term, KRR mitigates overfitting while leveraging kernel functions to transform the input space, making it a powerful tool for both regression and classification tasks. To delve deeper into the intricacies and applications of Kernel Ridge Regression, consider exploring the subsequent lessons provided below.