lime 0.4.1

• Add build-in support for parsnip and ranger
• Add preprocess argument to lime.data.frame to keep it in line with the other types. Use it to transform your data.frame into a new input that your model expects after permutations
• magick is now only in suggest to cut down on heavy hard dependencies
• explain now returns a tbl_df so you get pretty printing if you have tibble loaded
• When plotting regression explanations of non-binned features the feature weight is now multiplied by its value
• More consistent support for keras
• Fix bug when xgboost was used with with default objective
• Better errors when handling bad models
• plot_features now has a cases argument for subsetting the data before plotting

lime 0.4

• Add support for image explanation. The dispatch will be on paths pointing to valid image files. Image explanations can be visualised using plot_image_explanation (#35)
• Add support for neural networks from the keras package
• Add as_classifier() and as_regressor() for ad-hoc specification of the model type in case the heuristic implemented in lime doesn’t hold. as_classifier() also lets you add/overwrite the class labels.
• Use gower as the new default similarity measure for tabular data
• If bin_continuous = FALSE the default behavior is now to sample from a kernel density estimation rather than assume a normal distribution.
• Fix bug when numeric features in the training data were constant (#56)
• Fix bug when plotting regression explanations with plot_explanations() (#60)
• Logical columns in tabular data is now supported (#75)
• Overhaul of plot_text_explanation() with better formatting and scrolling support for many explanations
• All plots now show the fit of the explainer so the user can assess the quality of the explanation

lime 0.3.1

• Added a NEWS.md file to track changes to the package.
• Fixed bug when explaining regression models, due to drop=TRUE defaults (#33)
• Integer features are no longer converted to numeric during permutations (#32)
• Fix bug when working with xgboost and tabular predictions (@martinju #1)
• Training data can now contain NA values (#8)
• Keep ordering when plotting with plot_features() (#38)
• Fix support for mlr by extracting predictions correctly
• Added support for h2o (@mdancho84) (#40)
• Throws meaningful error when all permutations have 0 similarity to original observation (#47)
• Explaining data can now contain NA values (#45)
• Support for Date and POSIXt columns. They will be kept constant during permutations so that lime will explain the model behaviour at the given timepoint based on the remaining features (#39).
• Add plot_explanations() for an overview plot of a large explanation set