Which is the most important variable in random forest?
Unfortunately, like h2o the python random forest implementation offers only Gini importance, but this insightful post offers a solution: It has been known for while now that the Gini importance tends to inflate the importance of continuous or high-cardinality categorical variables:
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.
Why is feature importance important in random forest?
The feature importance (variable importance) describes which features are relevant. It can help with better understanding of the solved problem and sometimes lead to model improvements by employing the feature selection.
Why is a Random column not an important feature?
The only non-standard thing in preparing the data is the addition of a random column to the dataset. Logically, it has no predictive power over the dependent variable (Median value of owner-occupied homes in $1000’s), so it should not be an important feature in the model.
Is the Gini impurity used in random forests?
Random forests for classification might use two kind of variable importance. See the original description of the RF here. This is plain wrong. The gini impurity is build using only the proportions of the target/dependent variable, when split by a test which involves either numerical or nominal independent variable.
How is a linear regression different from a random forest?
For example, if I tell you that one ice-cream costs $1, 2 ice-creams cost $2, and 3 ice-creams cost $3, how much do 10 ice-creams cost? A linear regression can easily figure this out, while a Random Forest has no way of finding the answer.