While training a model, we should ensure that the model does not suffer from
- Over-fitting – high variance.
- Under-fitting – high bias.For example
Handling How to ?
- High Bias : underfitting – training / test error – high => Add more features better result. 
- High variance : overfitting – train error -low / test error – high
=> Feature reduction
It’s possible that we have too many features and that the model is overfitting, reducing the features will make the model more flexible.
=> Add more training examples can also help
Figure. Handling under / over fitting