- Make the model simpler
- Collect more data
- Collect more features
- Increase the regularization parameter
Answer – (c)
Underfitting is the opposite of overfitting and it occurs when model is too simple to learn from the given dataset. This could happen if right features were not selected or extracted, or the regularization was done with higher values of hyperparameters (used to control the amount of regularization). In underfitting, model is already simpler and should be made more powerful, with more parameters for instance. If you’re convinced that your model is powerful enough, try to increase the number of features or extract new features from the existing ones to solve underfitting problem. Once you’ve performed both the steps, reduce the regularization hyperparameter.