> ## Documentation Index
> Fetch the complete documentation index at: https://docs.clickml.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Feature Selection component

> Keep only the most informative features in your dataset using statistical tests, model-based importance, and recursive feature elimination methods.

The **Feature Selection** component removes irrelevant or redundant features. Fewer, better features typically lead to faster training and more generalizable models.

## Configuration

| Option                | Description                                                                                         | Default         |
| --------------------- | --------------------------------------------------------------------------------------------------- | --------------- |
| **Method**            | Selection algorithm (see table below)                                                               | —               |
| **Target Column**     | The column being predicted (required for supervised methods)                                        | —               |
| **K**                 | Number of top features to keep (supervised methods)                                                 | `10`            |
| **Threshold**         | Variance or correlation threshold (unsupervised methods)                                            | `0.0` / `0.9`   |
| **Estimator** *(RFE)* | Model used internally to rank features: `Random Forest`, `Logistic Regression`, `Linear Regression` | `Random Forest` |

### Methods

| Method                      | Type         | How it ranks features                                               |
| --------------------------- | ------------ | ------------------------------------------------------------------- |
| Variance Threshold          | Unsupervised | Drops features whose variance is below the threshold                |
| Correlation Threshold       | Unsupervised | Drops one of each pair of features correlated above the threshold   |
| Select K Best (Chi2)        | Supervised   | Ranks features by chi-squared statistic (non-negative values only)  |
| Select K Best (F-score)     | Supervised   | Ranks features by ANOVA F-score                                     |
| Select K Best (Mutual Info) | Supervised   | Ranks features by mutual information with the target                |
| RFE                         | Supervised   | Recursively removes the least important features using an estimator |
| Lasso (L1)                  | Supervised   | Drops features whose Lasso coefficient is zero                      |

## Input / Output

|        | Type                               |
| ------ | ---------------------------------- |
| Input  | DataFrame                          |
| Output | DataFrame (selected features only) |