Permutation feature importance package for browsers and Node.js

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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)

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``javascript
// 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.

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`javascript
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

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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

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