Nothing
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
. (#39) WARNING: Change in the default value of baseline
.yhat.*
functions help to handle additional parameters to different predict()
functions.HR
and HRTest
. Target variable is a factor with three levels. Is used in examples for classification.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)variable_response()
to better support of gbm
models (c8393120ffb05e2f3c70b0143c4e92dc91f6c823).plot_model_performance()
(e5e61d0398459b78ea38ccc980c4040fd853f449).breakDown
v 0.1.6.single_variable() / variable_response()
function uses predict_function
from explainer
(#17)explain()
function converts tibbles
to data.frame
when specified as data
argument (#15)explain.default()
should help when explain()
from dplyr
is loaded after DALEX
(#16)model_performance()
, variable_importance()
, variable_response()
, outlier_detection()
, prediction_breakdown()
. Old names are now deprecated but still working. (#12)apartments
- will be used in examplesvariable_importance()
allows work on full dataset if n_sample
is negativeplot_model_performance()
uses ecdf or boxplots (depending on geom
parameter).single_variable()
supports factor variables as well (with the use of factorMerger
package). Remember to use type='factor'
when playing with factors. (#10)explain()
. Old version has an argument predict.function
, now it's predict_function
. New name is more consistent with other arguments. (#7)xgboost
model (#11)variable_dropout()
function explain()
function implementedsingle_prediction()
function implementedsingle_variable()
function implementedAny scripts or data that you put into this service are public.
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