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