Contents
What does a variable importance plot show?
Variable importance plot provides a list of the most significant variables in descending order by a mean decrease in Gini. The top variables contribute more to the model than the bottom ones and also have high predictive power in classifying default and non-default customers.
What is the importance of variables?
In conclusion, variables are important because they help to measure concepts in a study. Because quantitative studies focus on measuring and explaining variables, choosing the right variables is important. The first step is to identify the correct variables to measure a property.
How is variable importance calculated Rpart?
From the rpart documentation, “An overall measure of variable importance is the sum of the goodness of split measures for each split for which it was the primary variable…” When rpart grows a tree it performs 10-fold cross validation on the data.
What is the importance of randomForest in R?
The randomForest package in R has two measures of importance. One is “total decrease in node impurities from splitting on the variable, averaged over all trees.”
What happens if a variable is not important in a random forest?
The idea is that if the variable is not important (the null hypothesis), then rearranging the values of that variable will not degrade prediction accuracy. Random forests use out-of-bag (OOB) samples to measure prediction accuracy.
How are predictors used in a random forest?
Random forests use out-of-bag (OOB) samples to measure prediction accuracy. In my experience, it does a pretty good job of finding the most important predictors, but it has issues with correlated predictors. For example, I was working on a problem where I was predicting the price that electricity trades.
Which is better supervised learning or random forest?
Random forests ™ are great. They are one of the best “black-box” supervised learning methods. If you have lots of data and lots of predictor variables, you can do worse than random forests. They can deal with messy, real data. If there are lots of extraneous predictors, it has no problem.