MLP NULL variable importance. The NULL importance measured by the decrease in a model score (i.e., Mean Decrease Accuracy (MDA), Mean Decrease in RMSE) when a variable is set as NULL variable.

MLP_varIMP_NULL_model(optmodel, feature_names = NULL, train_y = NULL, train_x = NULL, smaller_is_better = NULL, type = c("difference", "ratio"), nsim = 1, sample_size = NULL, sample_frac = NULL, verbose = FALSE, progress = "none", parallel = FALSE, paropts = NULL, ...)

Arguments

optmodel

The optimal model

feature_names

The variable names, should be the same as the dim of X

train_y

The Y variable (dependent variable) used in regression

train_x

The independent variable dataset

smaller_is_better

Indicating whether the importance is better for small values

type

whether "difference" or "ratio"

nsim

number of permutations used to estimate the permutation feature importance

Details

This function estimates the similar model agnostic feature importance but rather using time consuming permutation importance, it uses the Null model importance. Likewise, the best model is determined and the orignal variable metrics are used as the baseline. Then the NULL variable performance metrics are tested using the best model as the training set. This procedure directly set the target variable as NULL variable (Zero variance), thus the drop in the model score is indicative of how much the model depends on the variable.

Value

Return a list of scores, including MLP model decrease in accuracy, the permutation metrics, and the baseline metrics.

References

Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "Model-agnostic interpretability of machine learning." arXiv preprint arXiv:1606.05386 (2016). Fisher, Aaron, Cynthia Rudin, and Francesca Dominici. “Model Class Reliance: Variable importance measures for any machine learning model class, from the ‘Rashomon’ perspective.” http://arxiv.org/abs/1801.01489 (2018).

Author

qinxinghu@gmail.com

Note

See also