To evaluate the performance of different kernels in Nadaraya-Watson regression, a plotting utility can be utilized to visualize both the resulting regression estimates and the underlying attention weights. By passing various kernels to the estimator and plotting the predicted outputs against the true validation data alongside a heatmap of the attention matrix, one can directly observe how the choice of kernel influences the model's focus and its corresponding predictive accuracy.