Now that we have seen how overfitting occurs on our dataset, we will explore the impact of L2 regularization in reducing overfitting on the dataset.
L2 regularization on a dataset can be defined as follows:
Note that the loss function is the traditional loss function where y is the dependent variable, x is the independent variables, and W is the kernel (weight matrices).
The regularization term is added to the loss function. Note the regularization value is the sum of squared weight values across all the dimensions of a weight matrix. Given that we are minimizing the sum of squared value of weights along with ...
