importance

Permutation feature importance package for browsers and Node.js

Compute the relative importance of input variables of trained predictive models using feature shuffling

When called, the importance function shuffles each feature n times and computes the difference between the base score (calculated with original features X and target variable y) and permuted data. Intuitively that measures how the performance of a model decreases when we "remove" the feature.

• More info about the method: Permutation Feature Importance
• Permutation importance can be biased if features are highly correlated (Hooker, Mentch 2019)

Usage

// Create and train a model first
const rf = new RandomForestRegressor({
  maxDepth: 20,
  nEstimators: 50
})
rf.train(X, y)

// Get feature importance
const imp = importance(rf, X, y, {
  kind: 'mse',
  n: 10,
  means: true,
  verbose: false
})
console.log(impsRF)

You can also check example.js in this repo that uses the random-forest package as a predictive model.

API

importance(model, X, y, options)

• model - trained model with predict method (predictProba if cross-entropy used as score)
• X - 2D array of features
• y - 1D array of target variables

Options:

• kind - scoring function (mse, mae, rmse, smape, acc, ce (cross-entropy)
• n - number of times each feature is shuffled.
• means - if true returns only average importance
• verbose - if true throws some info into console

Feature selection

Feature importance is often used for variable selection. Permutation-based importance is a good method for that goal, but if you need more robust selection method check boruta.js

Web demo

The importance package is used for feature selection on StatSim Select and for data visualization on StatSim Vis