Which of the following can be used to overcome overfitting?
Answers
Explanation:
Cross-validation is a powerful preventative measure against overfitting. The idea is clever: Use your initial training data to generate multiple mini train-test splits. Use these splits to tune your model. In standard k-fold cross-validation, we partition the data into k subsets, called folds.
The methods used to overcome overfitting:
Overfitting occurs when a machine learning model performs well on the training data but does not generalize well to new, unseen data.
Cross-validation: This involves splitting the data into multiple subsets and training the model on different combinations of these subsets. By evaluating the model's performance on different subsets of data, we can get a better estimate of its ability to generalize to new, unseen data.
Regularization: This involves adding a penalty term to the loss function used to train the model, which encourages the model to have simpler, smoother solutions. This can help prevent overfitting by reducing the model's tendency to fit the noise in the training data.
Dropout: This involves randomly dropping out some of the neurons in the model during training. This can help prevent overfitting by encouraging the model to learn more robust and generalizable representations of the data.
Early stopping: This involves monitoring the model's performance on a validation set during training and stopping the training process when the model's performance on the validation set starts to degrade. This can help prevent overfitting by preventing the model from continuing to learn the noise in the training data.
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