Selecting meaningful features

If we notice that a model performs much better on a training dataset than on the test dataset, this observation is a strong indicator for overfitting. Overfitting means that model fits the parameters too closely to the particular observations in the training dataset but does not generalize well to real data—we say that the model has a high variance. A reason for overfitting is that our model is too complex for the given training data and common solutions to reduce the generalization error are listed as follows:

  • Collect more training data
  • Introduce a penalty for complexity via regularization
  • Choose a simpler model with fewer parameters
  • Reduce the dimensionality of the data

Collecting more training data is often not applicable. ...

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