forge {arf}

R Documentation

Forests for Generative Modeling

Description

Uses pre-trained FORDE model to simulate synthetic data.

Usage

forge(params, n_synth, parallel = TRUE)

Arguments

params	Parameters learned via forde.
n_synth	Number of synthetic samples to generate.
parallel	Compute in parallel? Must register backend beforehand, e.g. via doParallel.

Details

forge simulates a synthetic dataset of n_synth samples. First, leaves are sampled in proportion to their coverage. Then, each feature is sampled independently within each leaf according to the probability mass or density function learned by forde. This will create realistic data so long as the adversarial RF used in the previous step satisfies the local independence criterion. See Watson et al. (2022).

Value

A dataset of n_synth synthetic samples.

References

Watson, D., Blesch, K., Kapar, J., & Wright, M. (2022). Adversarial random forests for density estimation and generative modeling. arXiv preprint, 2205.09435.

See Also

adversarial_rf, forde

Examples

arf <- adversarial_rf(iris)
psi <- forde(arf, iris)
x_synth <- forge(psi, n_synth = 100)

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