Is Correlation good for feature selection?
How does correlation help in feature selection? Features with high correlation are more linearly dependent and hence have almost the same effect on the dependent variable. So, when two features have high correlation, we can drop one of the two features.
Do we need feature selection for decision tree?
At the end of the decision tree algorithm, features that are used for creating the tree are considered the most relevent ones. They do automatic selection of features. You do not need to implement one.
Why do we use correlation in feature selection?
Through correlation, we can predict one variable from the other. The logic behind using correlation for feature selection is that the good variables are highly correlated with the target. Furthermore, variables should be correlated with the target but should be uncorrelated among themselves.
What are the different types of feature selection?
There are two main types of feature selection techniques: supervised and unsupervised, and supervised methods may be divided into wrapper, filter and intrinsic. Filter-based feature selection methods use statistical measures to score the correlation or dependence between input variables that can be filtered to choose the most relevant features.
How to select features in a random forest?
We can also use RandomForest to select features based on feature importance. We calculate feature importance using node impurities in each decision tree. In Random forest, the final feature importance is the average of all decision tree feature importance. We could also have used a LightGBM.
How are statistical measures used in feature selection?
The statistical measures used in filter-based feature selection are generally calculated one input variable at a time with the target variable. As such, they are referred to as univariate statistical measures. This may mean that any interaction between input variables is not considered in the filtering process.