 added implementation of aSHAP (aggregated SHAP) and waterfall plot (#519)
 adding a new system for default color schemes (#541)
 added
cross_entropy
as model performance measure to multilabel settings #542
 removed the
yardstick
dependency
 new vignette added ‘How to use DALEX with the yardstick package?’
 new datasets from World Happiness Report:
happiness_test
and happiness_train
(#513)
 new datasets from COVID morality:
covid_summer
and covid_spring
(#513)
 changed URLs in the DESCRIPTION as requested in (#484)
 Fix model_info documentation (#498)
 Support for yardstic metrics (#495)
 Changed default in
explain(colorize=)
according to (#473)
 Added explain/yhat support for
partykit
(#438)

explain()
warns if target has more than two values for classification (#418)
 The
plot.model_performance_roc
, loss_one_minus_auc
and model_performance_auc
functions are rewritten to handle repeated predictions (#442)
 The
plot
function works for list of explanations (if possible) (#424)
 Order of explainer labels in different plots is the same. To get to this point, orders in
plot.model_performance(..., geom = "histogram" & "boxplot")
are reversed (#400)
 Fixed multiclass explainer when data has one column (#405)
 Now explainer handles R functions (#396)

predict_parts
function handles the N
argument natively (#394)
 All encouters of
nieghbour(s)
(ENspelling) were replaced with neighbor(s)
(USspelling) for the consistency and backword compatibility.
 Fixed bug when
predict_diagnostics
raised error if neighbor
value was higer than nrow(explainer$data)
.
 Added new parameter (
predict_function_target_column
) to explain
function that allows specifying positive class in binary classification tasks (#250).
 Fixed
model_diagnostics()
returning an error when data
is matrix
(#355)
 Fixed R package not working with Python Explainer (#318)
 Fixed
model_diagnostics()
returning an error when y_hat
or residuals
is of array
class (#319)
 Fixed grid lines in
theme_drwhy
on Windows
 Fixed logical values in y rising unnecessery warnings for classification task (#336)

plot.predict_diagnostics
now passess ellipsis to plot.ceteris_paribus_explainer
 This version requires
iBreakDown v1.3.1
and ingredients v1.3.1
 Fixed
plot.predict_parts
and plot.model_profile
(#277).
 Fixed
plot.model_profile
for multiple profiles (#237).
 External tests for not suggested packages added to ghactions (#237).
 Extended and refreshed documentation (#237).
 All dontrun statements changed to donttest according to CRAN policy.
 Added value for
s
parameter in yhat.glmnet
and yhat.cv.glmnet
.
 Fixed
model_diagnostics
passing wrong arguments to residual_function.
 Fixed aesthetic for
hist
geometry in plot.model_performance
using wrong arugments.

model_performance
will not work if model_info$type
is NULL
.
 Corrected description of
N
in model_parts
(#287).
 New warning messages for
y
parameter in explain
function.
 Solved bug in
yhat.ranger
causing predicts_parts
not to plot correctly when task is multiclass.

variable_effect
is now deprecated
 fixed typo in
predict_parts_oscillations_emp
 rewrite tests
 added
predict_parts
class to objects and plot.predict_parts
function
 added
model_parts
class to objects and plot.model_parts
function
 plot parameters added to the documentation
 Now in the
predict_profile
function one can specify how grid points shall be calculated, see variable_splits_type
(#267).
 The
predict_part
function has two new options for type: oscillations_uni
and oscillations_emp
(#267).
 The
plot.model_performance
function has a new geom="prc"
for Precision Recall curve (#273).

DALEX
now fully supports multiclass classification.

explain()
will use new residual function (1  true class probability) if multiclass classification is detected.

model_performance()
now support measures for multiclass classification.
 Remove
ggpubr
from suggests.

lossFunction
argument is now deprecated in plot.model_performance()
. Use the loss_function
argument.

model_profile
color changed to colors_discrete_drwhy(1)
which impacts the color of the line in plot.model_profile

loss_name
attribute added to loss functions. It will be passed to plot function for objects created with model_parts
.
 fixed tests and WARNINGs on CRAN

model_profile
for Accumulated Local rofiles by default use centering (center = TRUE
)
 deprecate
n_sample
argument in model_parts
(now it’s N
) (#175)

ingredients
and iBreakDown
are now imported by DALEX
 updated title for
plot.model_performance
(#160).
 in
explain
removed check related to duplicated target variable (#164).

variable_profile
calls ingredients::ceteris_paribus
(#131).

variable_response
and feature_response
moved to variable_effect
and now it calls ingredients::partial_dependency
(#131).

prediction_breakdown
moved to variable_attribution
and now it calls iBreakDown::break_down
(#131).
 updated
variable_importance
, not it calls the ingredients::variable_importance
(#131).
 updated
model_performance
(#130).
 added
yhat
for lrm
models from rms
package

theme_drwhy
has now left aligned title and subtitle.

residuals_distribution
calculates now diagnostic plots based on residuals (#143).

model_performance
calculates several metrics for classification and regression models (#146).

plot.model_performance
now supports ROC charts, LIFT charts, Cummulative Gain charts, histograms, boxplots and ecdf

residuals_distributon
is now individual_diagnostics
and produces objects of the class individual_diagnostics_explainers

plot.individual_diagnostics_explainers
now plots objects of the class individual_diagnostics_explainers

yhat
for caret models now returns matrix instead of data.frame

model_diagnostics
new function that plots residuals againes selected variable
 names of functions are changed to be compliant with latest version of the XAI pyramide
 updated
titanic_imputed
(#113).
 added
weights
to the explainer. Note that not all explanations know how to handle weights (#118).

yhat()
and model_info()
now support models created with gbm
package.
 new dataset
titanic_imputed
as requested in (#104).
 the
explain()
function now detects if target variable y
is present in the data
as requested in (#103).
 the DALEX GitHub repository is transfered from
pbiecek/DALEX
to ModelOriented/DALEX.
 Examples updated. Now they use only datasets available from DALEX.
 yhat.H2ORegressionModel and yhat.H2OBinomialModel moved to (DALEXtra) and merged into explain_h2o() function.
 yhat.WrappedModelmoved to (DALEXtra) and merged as explain_mlr() function.
 Wrapper for scikitlearn models restored in (DALEXtra) package.
 loss_one_minus_auc function added to loss_functions.R. It uses 1auc to compute loss. Function created by Alicja Gosiewska.
 Extension for DALEX avaiable at (DALEXtra)
 the
explain()
function is more verbose. With verbose = TRUE
(default) it prints detailed information about elements of an explainer (#95).
 New support for scikitlearn models via
scikitlearn_model()
 New
yhat
functions for mlr
, h2o
and caret
packages (added by Szymon).

plot.variable_importance_explainer()
has now desc_sorting
argument. If FALSE then variable importance will be sorted in an increasing order (#41).

pdp
, factorMerger
and ALEPlot
are going to Suggested
. (#60). In next releases they will be deprecated.
 added
predict
function that calls the predict_function
hidden in the explainer
object. (#58).
 the
titanic
dataset is copied from stablelearner
package. Some features are transformed (some NA
replaced with 0
, more numeric features).

DALEX
is being prepared for tighter integration with iBreakDown
and ingredients
.
 temporally there is a duplicated
single_variable
and single_feature
 Added new
theme_drwhy()
.
 New arguments in the
plot.variable_importance_explainer()
. Namely bar_width
with widths of bars and show_baseline
if baseline shall be included in these plots.
 New skin in the
plot.variable_response_explainer()
.
 New skin in the
plot.prediction_breakdown_explainer()
.
 Test datasets are now named
apartments_test
and HR_test
 For binary classification we return just a second column. NOTE: this may cause some unexpected problems with code dependend on defaults for DALEX 0.2.6.
 New versions of
yhat
for ranger
and svm
models.
 Residual distribution plots for model performance are now more legible when multiple models are plotted. The styling of plot and axis titles have also been improved (@kevinykuo).
 The defaults of
single_prediction()
are now consistent with breakDown::broken()
. Specifically, baseline
is now 0
by default instead of "Intercept"
. The user can also specify the baseline
and other arguments by passing them to single_prediction
(@kevinykuo, #39). WARNING: Change in the default value of baseline
.
 New
yhat.*
functions help to handle additional parameters to different predict()
functions.
 Updated
CITATION
info
 New dataset
HR
and HRTest
. Target variable is a factor with three levels. Is used in examples for classification.
 The
plot.model_performance()
has now show_outliers
parameter. Set it to anything >0 and observations with largest residuals will be presented in the plot. (#34)
 Small fixes in
variable_response()
to better support of gbm
models (c8393120ffb05e2f3c70b0143c4e92dc91f6c823).
 Better title for
plot_model_performance()
(e5e61d0398459b78ea38ccc980c4040fd853f449).
 Tested with
breakDown
v 0.1.6.
 The
single_variable() / variable_response()
function uses predict_function
from explainer
(#17)
 New names for some functions:
model_performance()
, variable_importance()
, variable_response()
, outlier_detection()
, prediction_breakdown()
. Old names are now deprecated but still working. (#12)
 A new dataset
apartments
 will be used in examples

variable_importance()
allows work on full dataset if n_sample
is negative

plot_model_performance()
uses ecdf or boxplots (depending on geom
parameter).
 Function
single_variable()
supports factor variables as well (with the use of factorMerger
package). Remember to use type='factor'
when playing with factors. (#10)
 Change in the function
explain()
. Old version has an argument predict.function
, now it’s predict_function
. New name is more consistent with other arguments. (#7)
 New vigniette for
xgboost
model (#11)
 Support for global model structure explainers with
variable_dropout()
function
 DALEX package is now public

explain()
function implemented

single_prediction()
function implemented

single_variable()
function implemented