Feature Importance
The feature importance module in the sovai library offers multiple unsupervised algorithms to quantify the significance of each feature in financial datasets.
Last updated
The feature importance module in the sovai library offers multiple unsupervised algorithms to quantify the significance of each feature in financial datasets.
Last updated
Tutorials are the best documentation — Feature Importance Tutorial
The module supports several methods for calculating feature importance:
Reflects how much each feature contributes to the variance in the randomly projected space.
Indicates how strongly each feature influences the approximation of non-linear relationships in the Fourier-transformed space.
Based on the magnitude of each feature's contribution to the extracted independent components, representing underlying independent signals in the data.
Determined by each feature's influence on the principal singular vectors, which represent directions of maximum variance in the data.
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.
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.
To calculate global feature importance across all methods:
Example of selecting top features based on importance scores:
