How is variable importance calculated?
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Similarly, how is variable importance calculated in random forest?
Gini-based importance For each variable, the sum of the Gini decrease across every tree of the forest is accumulated every time that variable is chosen to split a node. The sum is divided by the number of trees in the forest to give an average. The scale is irrelevant: only the relative values matter.
Subsequently, question is, why is a variable important? A variable is any element of an equation or experiment that can be changed. Variables are so important to science experiments and equations because they have a direct influence on the outcome of the experiment. A change in a variable, like temperature, can have a vast effect on the outcome of the experiment.
Beside above, how is variable importance calculated in GBM?
Variable Importance Calculation (GBM & DRF) Variable importance is determined by calculating the relative influence of each variable: whether that variable was selected to split on during the tree building process, and how much the squared error (over all trees) improved (decreased) as a result.
How do you calculate variable importance in decision tree?
Feature importance is calculated as the decrease in node impurity weighted by the probability of reaching that node. The node probability can be calculated by the number of samples that reach the node, divided by the total number of samples. The higher the value the more important the feature.