The Feature Extraction component applies dimensionality reduction algorithms to transform a set of input features into a smaller number of extracted components. This is useful for reducing noise, speeding up training, and visualizing high-dimensional data.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.
Configuration
| Option | Description | Default |
|---|---|---|
| Method | Reduction algorithm to use (see table below) | — |
| Columns | Numerical columns to reduce. Supports All Numerical Features. | All numerical |
| N Components | Number of output components to produce | 2 |
| Random State | Seed for reproducibility | 42 |
| Kernel (Kernel PCA only) | Kernel type: rbf, poly, sigmoid, cosine | rbf |
Methods
| Method | Description |
|---|---|
| PCA | Principal Component Analysis — finds the directions of maximum variance. The most common choice. |
| Kernel PCA | Non-linear version of PCA using a kernel function. |
| Truncated SVD | SVD-based reduction that works on sparse matrices. |
| ICA | Independent Component Analysis — separates statistically independent signals. |
| NMF | Non-Negative Matrix Factorization — requires all values to be non-negative. |
Input / Output
| Type | |
|---|---|
| Input | DataFrame |
| Output | DataFrame (original non-feature columns + new component columns) |
The output columns are named
pca_1, pca_2, ica_1, etc. depending on the method chosen. Non-selected columns are preserved.