# Feature Importance
`Tutorials` are the best documentation — [`Feature Importance Tutorial`](https://colab.research.google.com/github/sovai-research/sovai-public/blob/main/notebooks/computational/Feature%20Importance.ipynb)
### Feature Importance Methods
The module supports several methods for calculating feature importance:
#### Random Projection
```python
df_mega.importance("random_projection")
```
Reflects how much each feature contributes to the variance in the randomly projected space.
#### Random Fourier Features
```python
df_mega.importance("fourier")
```
Indicates how strongly each feature influences the approximation of non-linear relationships in the Fourier-transformed space.
#### Independent Component Analysis (ICA)
```python
df_mega.importance("ica")
```
Based on the magnitude of each feature's contribution to the extracted independent components, representing underlying independent signals in the data.
#### Truncated Singular Value Decomposition (SVD)
```python
df_mega.importance("svd")
```
Determined by each feature's influence on the principal singular vectors, which represent directions of maximum variance in the data.
#### Sparse Random Projection
```python
df_mega.importance("sparse_projection")
```
Based on how much each feature contributes to the variance in the sparsely projected space, similar to standard Random Projection but with improved computational efficiency.
#### Clustered SHAP Ensemble
```python
df_mega.importance("shapley")
```
Iteratively applies clustering, uses XGBoost to predict cluster membership, calculates SHAP values, and averages results across multiple runs to determine feature importance in identifying natural data structures.
### Global Feature Importance
To calculate global feature importance across all methods:
```python
df_mega.feature_importance()
```
### Feature Selection
Example of selecting top features based on importance scores:
```python
feature_importance = df_mega.importance("sparse_projection")
df_select = df_mega[feature_importance["feature"].head(25)]
```
.png?alt=media)
.png?alt=media)