Hands-On Python Deep Learning for the Web

Overfitting and underfitting

When an ML model performs very well on the training data but poorly on the data from either the test set or validation set, the phenomenon is referred to as overfitting. There can be several reasons for this; the following are the most common ones: 

• The model is very complex with respect to the data. A decision tree with very high levels and a neural network with many layers are good examples of model complexity in this case. 
• The data has lots of features but very few instances of the population. 

In ML literature, the problem of overfitting is also treated as a problem of high variance. Regularization is the most widely used approach to prevent overfitting.

We have already discussed the concept of bias. A ...