In this chapter, we will cover the following topics:
- Why do models need to be evaluated?
- Different methods for model evaluation
- Estimating model performance with k-fold cross-validation
- Estimating model performance with Leave One Out Cross Validation
- Performing cross-validation with the e1071 package
- Performing cross-validation with the caret package
- Ranking the variable importance with the caret package
- Ranking the variable importance with the rminer package
- Finding highly correlated features with the caret package
- Selecting features using the caret package
- Measuring the performance of a regression model
- Measuring the prediction performance with the confusion matrix
- Measuring the prediction performance using ROCR
- Comparing an ROC curve using the caret package
- Measuring performance differences between models with the caret package
