lime 0.5.2
- Fixed use of
order()
on data.frame
objects
- Moved htmlwidgets, shiny, and shinythemes to suggests
lime 0.5.1
- Fixed namespace import from glmnet following changes there
lime 0.5.0
explain()
will now pass ...
on to the relevant predict()
method (#150)
explain.data.frame()
gains a gower_pow
argument to modify the calculated gower distance before use by raising it to the power of the given value (#158)
- Fixed a bug when calculating R^2 on single feature explanations (@pkopper, #157)
- Fixed formatting of text prediction html presentation (#145)
- Fixed a bug when setting feature select method to “none” (#141)
- Changes default colouring from green-red to blue-red (#137)
lime()
now warns when quantile binning is not feasible and uses standard binning instead (#154)
- Changed the
lambda
value in the local model fit to match the one used in the Python version according to the relationship given here: https://stats.stackexchange.com/a/270705
- Added pkgdown site at https://lime.data-imaginist.com
- Fixed a bug when using a proprocessor with data.frame explanations
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