What is the variable importance?
(My) definition: Variable importance refers to how much a given model “uses” that variable to make accurate predictions. The more a model relies on a variable to make predictions, the more important it is for the model. It can apply to many different models, each using different metrics.
How do you use variable importance?
Variable importance is calculated by the sum of the decrease in error when split by a variable. Then, the relative importance is the variable importance divided by the highest variable importance value so that values are bounded between 0 and 1.
How does the variable importance of a predictor work?
For most classification models, each predictor will have a separate variable importance for each class (the exceptions are classification trees, bagged trees and boosted trees). All measures of importance are scaled to have a maximum value of 100, unless the scale argument of varImp.train is set to FALSE.
How is the variable importance measure used in regression?
For a specific class, the maximum area under the curve across the relevant pair-wise AUC’s is used as the variable importance measure. For regression, the relationship between each predictor and the outcome is evaluated. An argument, nonpara, is used to pick the model fitting technique.
Which is an example of a variable importance?
5.2An Example 5.3Basic Parameter Tuning 5.4Notes on Reproducibility 5.5Customizing the Tuning Process 5.5.1Pre-Processing Options 5.5.2Alternate Tuning Grids 5.5.3Plotting the Resampling Profile 5.5.4The trainControlFunction 5.5.5Alternate Performance Metrics 5.6Choosing the Final Model 5.7Extracting Predictions and Class Probabilities
What can a tree based variable be used for?
Tree Based Variable Importance A Decision Tree crawls through your data, one variable at a time, and attempts to determine how it can split the data into smaller, more homogeneous buckets. It can be used for either numeric or categorical prediction.