Contents
How do you read variable importance in random forest?
The default method to compute variable importance is the mean decrease in impurity (or gini importance) mechanism: At each split in each tree, the improvement in the split-criterion is the importance measure attributed to the splitting variable, and is accumulated over all the trees in the forest separately for each …
What is the variable importance plot?
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.
Which is better random forest or variable selection?
In the case of random forest, I have to admit that the idea of selecting randomly a set of possible variables at each node is very clever. The performance is much better, but interpretation is usually more difficult. And something that I love when there are a lot of covariance, the variable importance plot.
How is a random forest plot different from a scatter plot?
It is different than scatter plot of X vs. Y as scatter plot does not isolate the direct relationship of X vs. Y and can be affected by indirect relationships with other variables on which both X and Y depend. 1. train a random forest model (let’s say F1…F4 are our features and Y is target variable. Suppose F1 is the most important feature).
What is the outcome of the random forest?
In this instance, the outcome is whether a person has an income above or below $50,000. There are two measures of importance given for each variable in the random forest. The first measure is based on how much the accuracy decreases when the variable is excluded. This is further broken down by outcome class.
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.”