Evaluate the Performance During Training and Prevent Overfitting
Cross-validation: Cross-validation is a technique used to evaluate the performance of a model during training. It involves partitioning the available data into multiple subsets, or folds, and using each fold as a validation set while the remaining data is used for training. By repeating this process multiple times with different folds, cross-validation estimates the model's performance on unseen data and helps prevent overfitting.
Early stopping: Early stopping is a technique used to prevent overfitting by monitoring the model's performance on a validation set during training and stopping the training process when the validation loss starts to increase. This prevents the model from overfitting to the training data and improves its ability to generalize to new data.
Regularization: Regularization is a technique used to prevent overfitting by adding a penalty term to the model's loss function that discourages the model from learning complex or noisy patterns in the data. This penalty term can take the form of L1 or L2 regularization, which respectively encourage sparsity or reduce the magnitude of the model's weights.
Dropout: Dropout is a regularization technique used in neural networks that randomly drops out some of the nodes in each layer during training. This helps to prevent the network from overfitting by forcing it to learn more robust and generalizable representations.
Batch normalization: Batch normalization is a technique used to prevent overfitting by normalizing the inputs to each layer in a neural network. This helps to reduce the effect of covariate shift and stabilizes the optimization process, improving the generalization performance of the model.
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