What is Dropout?
Dropout refers to randomly turning off hidden units so that a smaller network is trained on a given pass
Dropout refers to randomly turning off hidden units so that a smaller network is trained on a given pass
Overfitting can be mitigated by reducing model complexity, adding regularization, doing feature selection, getting more training data, and performing cross validation
A model that is underfit will produce evaluation metrics that are poor on the training data alone, such as high RMSE or misclassification rate.
Overfitting occurs when a machine learning model becomes too complex and starts fitting the training data too closely thereby leading to poor performance on new, unseen data.
Tuning the combination of number of trees and learning rate is a good way to ensure you are creating a model with appropriate complexity.
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