# What is correlation based feature selection?

## What is correlation based feature selection?

Correlation based feature selection (CFS) methodology is applied to the original dataset for finding relevant class features. Fast correlation based filter method is applied to continuous and discrete problems. Features are selected using relief algorithm to reduce the dimensionality.

## How does correlation Help 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.

**What are the three types of feature selection methods?**

There are three types of feature selection: Wrapper methods (forward, backward, and stepwise selection), Filter methods (ANOVA, Pearson correlation, variance thresholding), and Embedded methods (Lasso, Ridge, Decision Tree).

### What is p-value in feature selection?

p-value refers to the hypothesis of the significance level. Let’s say you have a friend who says that a feature is absolutely of no use. (that is called as null hypothesis). The higher the p-value’s value is, the more he is correct and vice versa. p-value goes from 0 to 1.

### What are the feature selection methods?

There are two main types of feature selection techniques: supervised and unsupervised, and supervised methods may be divided into wrapper, filter and intrinsic.

**What are the feature selection techniques?**

It can be used for feature selection by evaluating the Information gain of each variable in the context of the target variable.

- Chi-square Test.
- Fisher’s Score.
- Correlation Coefficient.
- Dispersion ratio.
- Backward Feature Elimination.
- Recursive Feature Elimination.
- Random Forest Importance.

#### Is PCA better than feature selection?

Both PCA and feature selection are great! The choice of one of the techniques or both depends on your goal. When you work with PCA the data will be transformed, which is great for dimension reduction and could result in better regression models.

#### How does feature selection work?

Feature selection is the process of reducing the number of input variables when developing a predictive model. 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.

**What is p-value formula?**

P-value defines the probability of getting a result that is either the same or more extreme than the other actual observations. The P-value represents the probability of occurrence of the given event. The formula to calculate the p-value is: Z=^p−p0√p0(1−p0)n Z = p ^ − p 0 p 0 ( 1 − p 0 ) n.