mactivate-package: m-activation

In mactivate: Multiplicative Activation

Description

Provides methods and classes for adding m-activation ("multiplicative activation") layers to MLR or multivariate logistic regression models. M-activation layers created in this library detect and add input interaction (polynomial) effects into a predictive model. M-activation can detect high-order interactions – a traditionally non-trivial challenge. Details concerning application, methodology, and relevant survey literature can be found in this library's vignette, "About."

Details

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Please make sure to read Details in f_dmss_dW help page before using this library. This package allows the user to extend the usual multivariate regression solution by adding (parallel) multiplicative “activation layers.” These activation layers can be very useful for identifying input interactions, and, if the user wishes, transparently test the appropriateness of input transformations. Three functions are provided for fitting data, f_fit_hybrid_01 and f_fit_gradient_01 for a numeric response (usual MLR), and f_fit_gradient_logistic_01 for a binary response (multivariate logistic regresssion). The user is encouraged to consult the “About” vignette as well as the examples available in the respective functions' documentation for details about m-activation and practical examples of implementation.

Author(s)

Dave Zes

Maintainer: Dave Zes <[e-mail]>

Examples

## please see docs for individual functions.

mactivate documentation built on Aug. 2, 2021, 5:07 p.m.