Dealing with reducible error components

High bias:

• Add more features

• Apply a more complex model

• Use less instances to train

• Reduce regularization

High variance:

• Conduct feature selection and use less features

• Get more training data

• Use regularization to help overcome the issues due to complex models

Cross validation

Cross-validation is an important step in the model validation and evaluation process. It is a technique to validate the performance of a model before we apply it on an unobserved dataset. It is not advised to use the full training data to train the model, because in such a case we would have no idea how the model is going to perform in practice. As we learnt in the previous section, a good learner should be able to generalize well on an unseen dataset; that can happen only if the model is able to extract and learn the underlying patterns or relations among the dependent and independent attributes. If we train the model on the full training data and apply the same on a test data, it is...