# How is variable importance calculated?

**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.

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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**.