Answer Posted / Swati Rajput

To avoid overfitting, we can use techniques like cross-validation, regularization, and early stopping. Cross-validation helps us to evaluate the generalization performance of our model by splitting the data into multiple folds and testing on each fold. Regularization adds a penalty term to the loss function, which discourages complex models that fit noise in the training data. Early stopping stops the training process when the validation error starts increasing, indicating that the model is starting to overfit.

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