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Variable Importance Methods for kindling Models

Source: R/variable_imp.R
kindling-varimp.Rd

This file implements methods for variable importance generics from NeuralNetTools and vip packages.

Usage

# S3 method for class 'ffnn_fit'
garson(mod_in, bar_plot = FALSE, ...)

# S3 method for class 'ffnn_fit'
olden(mod_in, bar_plot = TRUE, ...)

# S3 method for class 'ffnn_fit'
vi_model(object, type = c("olden", "garson"), ...)

Arguments

mod_in

A fitted model object of class "ffnn_fit".

bar_plot

Logical. Whether to plot variable importance (default TRUE).

...

Additional arguments passed to methods.

object

A fitted model object of class "ffnn_fit".

type

Type of algorithm to extract the variable importance. This must be one of the strings:

  • 'olden'

  • 'garson'

Value

A data frame for both "garson" and "olden" classes with columns:

x_names

Character vector of predictor variable names

y_names

Character string of response variable name

rel_imp

Numeric vector of relative importance scores (percentage)

The data frame is sorted by importance in descending order.

A tibble with columns "Variable" and "Importance" (via vip::vi() / vip::vi_model() only).

Garson's Algorithm for FFNN Models

{kindling} inherits NeuralNetTools::garson to extract the variable importance from the fitted ffnn() model.

Olden's Algorithm for FFNN Models

{kindling} inherits NeuralNetTools::olden to extract the variable importance from the fitted ffnn() model.

Variable Importance via {vip} Package

You can directly use vip::vi() and vip::vi_model() to extract the variable importance from the fitted ffnn() model.

References

Beck, M.W. 2018. NeuralNetTools: Visualization and Analysis Tools for Neural Networks. Journal of Statistical Software. 85(11):1-20.

Garson, G.D. 1991. Interpreting neural network connection weights. Artificial Intelligence Expert. 6(4):46-51.

Goh, A.T.C. 1995. Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering. 9(3):143-151.

Olden, J.D., Jackson, D.A. 2002. Illuminating the 'black-box': a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling. 154:135-150.

Olden, J.D., Joy, M.K., Death, R.G. 2004. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling. 178:389-397.

Examples

# \donttest{
if (torch::torch_is_installed()) {
    model_mlp = ffnn(
        Species ~ .,
        data = iris,
        hidden_neurons = c(64, 32),
        activations = "relu",
        epochs = 100,
        verbose = FALSE,
        cache_weights = TRUE
    )
    
    # Directly use `NeuralNetTools::garson`
    model_mlp |>
        garson()
    
    # Directly use `NeuralNetTools::olden`    
    model_mlp |>
        olden()
} else {
    message("Torch not fully installed — skipping example")
}

#>        x_names y_names     rel_imp
#> 1 Sepal.Length       y -0.11604490
#> 2  Petal.Width       y  0.03564795
#> 3 Petal.Length       y  0.02774481
#> 4  Sepal.Width       y  0.02440405
# }

# \donttest{
# kindling also supports `vip::vi()` / `vip::vi_model()`
if (torch::torch_is_installed()) {
    model_mlp = ffnn(
        Species ~ .,
        data = iris,
        hidden_neurons = c(64, 32),
        activations = "relu",
        epochs = 100,
        verbose = FALSE,
        cache_weights = TRUE
    )

    model_mlp |>
        vip::vi(type = 'garson') |>
        vip::vip()
} else {
    message("Torch not fully installed — skipping example")
}

# }