explain_tidysdm {tidysdm} | R Documentation |
Create explainer from your tidysdm ensembles.
Description
DALEX is designed to explore and explain the behaviour of Machine Learning
methods. This function creates a DALEX explainer (see DALEX::explain()
), which can then be queried
by multiple function to create explanations of the model.
Usage
explain_tidysdm(
model,
data,
y,
predict_function,
predict_function_target_column,
residual_function,
...,
label,
verbose,
precalculate,
colorize,
model_info,
type,
by_workflow
)
## Default S3 method:
explain_tidysdm(
model,
data = NULL,
y = NULL,
predict_function = NULL,
predict_function_target_column = NULL,
residual_function = NULL,
...,
label = NULL,
verbose = TRUE,
precalculate = TRUE,
colorize = !isTRUE(getOption("knitr.in.progress")),
model_info = NULL,
type = "classification",
by_workflow = FALSE
)
## S3 method for class 'simple_ensemble'
explain_tidysdm(
model,
data = NULL,
y = NULL,
predict_function = NULL,
predict_function_target_column = NULL,
residual_function = NULL,
...,
label = NULL,
verbose = TRUE,
precalculate = TRUE,
colorize = !isTRUE(getOption("knitr.in.progress")),
model_info = NULL,
type = "classification",
by_workflow = FALSE
)
## S3 method for class 'repeat_ensemble'
explain_tidysdm(
model,
data = NULL,
y = NULL,
predict_function = NULL,
predict_function_target_column = NULL,
residual_function = NULL,
...,
label = NULL,
verbose = TRUE,
precalculate = TRUE,
colorize = !isTRUE(getOption("knitr.in.progress")),
model_info = NULL,
type = "classification",
by_workflow = FALSE
)
Arguments
model |
object - a model to be explained |
data |
data.frame or matrix - data which will be used to calculate the explanations. If not provided, then it will be extracted from the model. Data should be passed without a target column (this shall be provided as the |
y |
numeric vector with outputs/scores. If provided, then it shall have the same size as |
predict_function |
function that takes two arguments: model and new data and returns a numeric vector with predictions. By default it is |
predict_function_target_column |
Character or numeric containing either column name or column number in the model prediction object of the class that should be considered as positive (i.e. the class that is associated with probability 1). If NULL, the second column of the output will be taken for binary classification. For a multiclass classification setting, that parameter cause switch to binary classification mode with one vs others probabilities. |
residual_function |
function that takes four arguments: model, data, target vector y and predict function (optionally). It should return a numeric vector with model residuals for given data. If not provided, response residuals ( |
... |
other parameters |
label |
character - the name of the model. By default it's extracted from the 'class' attribute of the model |
verbose |
logical. If TRUE (default) then diagnostic messages will be printed |
precalculate |
logical. If TRUE (default) then |
colorize |
logical. If TRUE (default) then |
model_info |
a named list ( |
type |
type of a model, either |
by_workflow |
boolean determining whether a list of explainer, one per model, should be returned instead of a single explainer for the ensemble |
Value
explainer object DALEX::explain
ready to work with DALEX
Examples
# using the whole ensemble
lacerta_explainer <- explain_tidysdm(tidysdm::lacerta_ensemble)
# by workflow
explainer_list <- explain_tidysdm(tidysdm::lacerta_ensemble,
by_workflow = TRUE
)