# Feature Selection

Last Updated: 2021-11-19

## Feature Selection

- Embedded approaches: data mining algorithm itself decides which attributes to use. e.g. decision tree classifier
- Filter approaches: before data mining algorithm is run; independent of the data mining task. e.g. pairwise correlation is as low as possible
- Wrapper: use data mining algorithm as black box

### Feature Selection Architecture

- a measure for evaluating a subset
- a strategy that controls the generation of a new subset of features
- a stopping criterion
- a validation procedure

## mRMR (minimum Redundancy Maximum Relevance Feature Selection)

## Univariate Selection

One variable + One target

http://blog.datadive.net/selecting-good-features-part-i-univariate-selection/

Reasons of feature selection:

- Reducing the number of features, to reduce overfitting and improve the generalization of models.
- To gain a better understanding of the features and their relationship to the response variables.

### Pearson Correlation

A value between -1 and 1

- -1: perfect negative correlation
- +1: perfect positive correlation
- 0: no linear correlation

Use `pearsonr`

in `Scipy`

: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html

```
from scipy.stats import pearsonr
pearsonr(x, y)
```

Pros:

- fast to calculate
- returned value [-1, 1] instead of [0, 1], extra negative/positive info

Cons:

- only sensitive to
**linear**relationship.

### Maximal Information Coefficient

https://en.wikipedia.org/wiki/Maximal_information_coefficient

- Searches for optimal binning and turns mutual information score into a metric that lies in range [0;1].
- Linear or non-linear.

### Distance correlation

While for Pearson correlation, the correlation value 0 does not imply independence, distance correlation of 0 does imply that there is no dependence between the two variables.

## Linear Model And Regularization

http://blog.datadive.net/selecting-good-features-part-ii-linear-models-and-regularization/

## Random Forest

http://blog.datadive.net/selecting-good-features-part-iii-random-forests/