Model Diagnostics, Machine Learning.
parameters to avoid fitting noise; identify and utilize only the most relevant features, discarding less informative ones. Regularization: Add penalty terms to the loss function to discourage large weights, preventing the model from becoming too complex (L1 and L2 Regularization); randomly