Nov 22, 2022
11 stories
14 saves
Method 01: Learn how to apply PCA to prevent overfitting in a logistic regression model.
Method 02: Learn how to perform feature selection to prevent overfitting in a random forest classifier.
Method 03 (a): Learn how to use early stopping to reduce overfitting in neural networks using the Keras EarlyStopping() function.
Method 03 (b): Learn how to use early stopping to reduce overfitting in CatBoost.
Method 04: Learn how to apply k-fold cross-validation to mitigate overfitting in a decision tree model.
Method 05: Learn how to build a random forest (an ensemble of decision trees) to prevent overfitting in decision tree models.
Methods 06, 07: Learn how to apply pre-pruning and post-pruning methods to prevent overfitting in decision trees.
Method 08: Learn how to apply noise regularization to prevent overfitting in neural networks using the Keras GaussianNoise layer.
Method 09: Learn how to apply dropout regularization to prevent overfitting in neural networks using the Keras dropout class.
Method 10 (a): Learn how to apply L1 and L2 regularization methods to prevent overfitting in neural networks using the Keras Regularizers module (API).
Method 10 (b): Learn how to apply L1 and L2 regularization methods to prevent overfitting in linear and logistic regression models.
