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in Machine Learning by
Q:

How can you avoid overfitting?

2 Answers

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Overfitting happens when a machine has an inadequate dataset and it tries to learn from it. So, overfitting is inversely proportional to the amount of data.

For small databases, we can bypass overfitting by the cross-validation method. In this approach, we will divide the dataset into two sections. These two sections will comprise testing and training sets. To train the model, we will use the training dataset and, for testing the model for new inputs, we will use the testing dataset.

This is how we can avoid overfitting.

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Overfitting refers to a model that is only set for a very small amount of data and ignores the bigger picture. There are three main methods to avoid overfitting:

  • Keep the model simple—take fewer variables into account, thereby removing some of the noise in the training data
  • Use cross-validation techniques, such as k folds cross-validation 
  • Use regularization techniques, such as LASSO, that penalize certain model parameters if they're likely to cause overfitting

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