- Remove 
reticulate from imports. 
- Refactor 
create_env. 
- fixed 
explain_tidymodels to ignore
residual_function for classification models. 
- fixed 
explain_h2o examples that might occasionally
crash. 
- bump the requirement for 
DALEX to 2.4.0. 
- remove 
randomForest from suggest due to it enforcing R
v4.1 (changed to ranger). 
- fix 
predict_surrogate() when
new_observation has too many variables (e.g. target
outcome). 
- auto-convert the 
mlr3 learner-like objects with
mlr3::as_learner() in explain_mlr3(). 
- Skip 
explain_keras and explain_scikitlearn
examples while running on macOS as they can rise false-positive errors
during R CMD check for some versions of macOS. The very same code still
executes properly in tests. 
- Skip check if the model is trained in
explain_tidymodels if the model inherits from
model_fit class. 
- Add support for stacked tidymodels (
stacks
package). 
- Add 
dalex_load_explainer function. 
- Clear up documentation.
 
- Fix errors coming from the new reticulate version
 
- Adjust explain functions to DALEX 2.1
 
explain_tidymodels() added as a support for tidymodels
workflows. 
- Removed aspect importance. It’s available in triplot package
https://cran.r-project.org/web/packages/triplot/index.html.
 
predict_surrogate() function is added to provide easier
interface of accessing lime/iml/localModel implementations of the LIME
method. 
- In added 
yhat.GraphLearner() and
model_info.GraphLearner() to handle GraphLearners
mlr3 objects. 
- New examples.
 
- In 
explain_h2o() data parameter will bo converted to
data.frame if H2OFrame object was passed. 
- Aspect importance related functions set deprecated. Will be removed
with next release.
 
explain_xgboost() function added 
- DALEXtra now supports multiclass classification (accordingly to
DALEX >= 1.3)
 
funnel_mesure() and
training_test_comparison() recognizes type of the task and
applies proper loss_function 
yhat.WrappedModel() returns factor response if
predict.type is not prob. 
explain_h2o() now supports model as
H2OAutoML 
- Removed h2o::init() from explain_h2o()
 
- Removed mljar support as mljar package is not available for R
3.6.2
 
- Ajusted to DALEX 1.0
 
- fixed 
yhat.LearnerClassif() returning wrong column of
probabilities (PR #34, thanks Hubert!) 
- Rebuilded 
plot.overall_comparison() (I lack words that
could describe Your greatness, Ania!). 
- New README and DESCRIPTION. They are more accurate now.
 
- Small fixes to 
funnel_measure() that imporves it’s
stability. 
- New plot function for 
funnel_measure() objects. (Thanks
Anna Kozak, You are awesome!). 
- New tests for 
funnel_measure() and
plot.funnel_measure() (Once again You are awesome,
Ania!). 
- Added 
aspect_importnace from ingredients
(#19) 
- Support for 
mlr3 added 
- DALEXtra now depends DALEX (0.4.9)
 
- Ceiling replaced with round in 
funnel_measure() 
champion_challenger(). 
overall_comparison() added with generic plot and print
functions. 
training_test_comparison() added with generic plot and
print functions. 
funnel_measure() added with generic plot and print
functions. 
- test for h2o rebuilded.
 
explain_keras() added. 
explain_mljar() added. 
- documentation refreshed with links to functions.
 
explain_scikitlearn() rebuilded. Some of the code was
exported to inner functions (helper_functions.R). 
- conda installation in 
README.md. 
scikitlearn_unix.yml file renamed to
testing_environment.yml. 
explain_scikitlearn() rebuilded. Now class
scikitlearn_model is a additional class for original Python object
instead of another object. 
- explainers created with 
explain_scikitlearn() have
addidtional field param_set. 
yhat() is now generic. 
- New examples in 
README.md. 
- Now when you pass .yml that consist environment name that already
exists one the machine, DALEXtra will not rise an error and contiune
work with existing env.
 
- If condaenv is NULL when creating_env on unixlike OS, DALEXtra will
try to find conda on his own.
 
on_attach() function now checks if conda is installed.
Alert is rised if not. 
- yhat.R created. Predict functions are stored there in order to be
more accesible.
 
explain_h2o() and explain_mlr()
rebuilded. 
- travis and codecov is now aviable available for DALEXtra.
 
- tests added.
 
scikitlearn_unix.yml file added to external data. This
helps testing using linuxlike OS. 
- few minor updates in the documentation.
 
- message in 
create_env() changed. 
explain_mlr() function implemented. 
explain_h2o() function implemented. 
- DALEXtra package is now public.
 
explain_scikitlearn() function implemented. 
create_env() function implemented.