Interpretable Machine Learning

Interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are: Feature importance described by Fisher et al. (2018) , accumulated local effects plots described by Apley (2018) , partial dependence plots described by Friedman (2001) < http://www.jstor.org/stable/2699986>, individual conditional expectation ('ice') plots described by Goldstein et al. (2013) , local models (variant of 'lime') described by Ribeiro et. al (2016) , the Shapley Value described by Strumbelj et. al (2014) , feature interactions described by Friedman et. al and tree surrogate models.


News

iml 0.7.1

  • Fixes problems with wrong computation of feature importance, features effects and so on for xgboost models.

iml 0.7.0

  • The Partial class is deprecated and will be removed in future versions. You should use FeatureEffect now. Its usage is similar to Partial but the aggregation and ice argument are now combined in the new method argument, where you can choose between 'ale', 'pdp', 'ice', 'pdp+ice'.
  • Introduced ALE plots into the FeatureEffect class (method='ale'). They are now the default instead of PDPs, because they are faster and unbiased.
  • Plot for categorical features in PDP changed. Now showing bar plots instead of boxplots when method='pdp'

iml 0.6

  • Removed losses: f1, logLoss, rmse, mdae, rae, rmse, rmsle, rse, rrse f1 because the implementation used didn't make sense anyways
  • Interaction: The results return as interaction strength now the H-statistic instead of the H-squared-statistic. This makes it more coherent with the gbm pacakge and the interact.gbm function and with what Friedman uses in the plots in the paper. For users of the package this means that an interaction of strength x becomes an interaction of strength sqrt(x).
  • Interaction, FeatureImp and Partial are now computed batch-wise in the background. This prevents this methods from overloading the memory. For that, the Predictor has a new init argument 'batch.size' which limits the number of rows send to the model for prediction for the methods Interaction, FeatureImp and Partial.
  • Interaction and FeatureImp additionally allow parallel computation on multiple cores. See vignette("parallel", package = "iml") for how to use it.

iml 0.5.2

  • The Predictor can be initialized with a type (e.g. type = "prob"), which is more convenient than writing a custom predict.fun. For caret classification models, the default is now to return the response, so make sure to initialize the Predictor with type = "prob" for fine-grained results.
  • It's easier to use classifier that output class labels and no probabilities. No warning will be issued anymore. Internally, the class labels are treated as probabilities (one column per class), where the probability for the predicted class is 1, for the others 0.
  • FeatureImp supports the n.repetitions parameter which controls the number of repetitions of the feature shuffling.

iml 0.5.0/1

  • Implemented Interaction measure
  • Removed feature.index variable from Partial and renamed .class.name column in results to .class.

iml 0.4.0

  • object$run() does not return self any longer. This means using object$set.feature() for example does not automatically print the object summary any longer.
  • Added an introductory vignette.
  • Fixed an issue where the Predictor would not store X, when y is given as character.
  • The column names of the data.frames with the results of the interpretation methods start with "." instead of "..". This is due to a recent change in the data.table package v1.10.5 news item 18.
  • Removed the deprecated classes PartialDependence and Ice. Use Partial instead.

iml 0.3.0

  • FeatureImp$results column permutationError renamed to permutation.error
  • Allow setting distance function in LocalModel
  • Merge the classes Ice and PartialDependence into Partial
    • The newly introduced Partial class can plot ice and pd curves, also in the same plot
    • It is now possible to center partial dependence plots
    • In obj$results has a new column "type" which contains either "ice" or "pdp". The column ..individual was renamed to "..id" and "y.hat" has been renamed to "..y.hat".
    • Ice and PartialDependence will be deprecated starting from 0.4.x
    • Adds argument and field types in the documentation

iml 0.2

  • The API has been reworked:
    • User directly interacts with R6 classes (pdp() is now PartialDependence$new()).
    • User has to wrap the machine learning model with Predictor$new().
    • New data points in Shapley and LocalModel can be set with $explain().
    • Lime has been renamed to LocalModel.
  • Plots have been improved.
  • Documentation has been improved.

iml 0.1

Initial release

Reference manual

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install.packages("iml")

0.7.1 by Christoph Molnar, 2 months ago


https://github.com/christophM/iml


Report a bug at https://github.com/christophM/iml/issues


Browse source code at https://github.com/cran/iml


Authors: Christoph Molnar [aut, cre]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports R6, checkmate, ggplot2, partykit, glmnet, Metrics, data.table, foreach, yaImpute

Suggests randomForest, gower, testthat, rpart, MASS, caret, e1071, knitr, mlr, covr, rmarkdown, devtools, doParallel, ALEPlot, ranger


See at CRAN