Mastering Text Mining with R

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 ...