plotImportance: Importance Plot

In Pizzaknoedel/visualize-hyperparameter: Visualizing Hyperparamer Performance Dependencies

Description

Visualization of an importance plot from a mlr3::Task object and a mlr3::Learner.

Usage

plotImportance(task, learner = NULL, loss = "mae", title = TRUE)

Arguments

task	mlr3::Task object A task for which the plot should be generated.
learner	mlr3::Learner object A learner for which the plot should be generated. If NULL, a random forest for regression or classification will be used. The default is "regr.ranger" for regression tasks and "classif.ranger" for classification tasks.
loss	(character(1) The loss function to be used. It can be chosen between "ce", "f1", "mae", "mse", "rmse", "mape", "mdae", "msle", "percent_bias", "rae", "rmse", "rmsle", "rse", "rrse", "smape". The default is "mae" for a Regression task. For for a classification task only "ce" is available.
title	(logical) If TRUE, the title will be plotted. The default is TRUE.

Value

A plotly object.

See Also

plotParallelCoordinate plotHeatmap plotPartialDependence

Examples

library(mlr3)
data(glmnet_ela)
task_glmnet_ela = TaskRegr$new(id = "task_glmnet", backend = glmnet_ela, target = "logloss")
plotImportance(task_glmnet_ela)

Pizzaknoedel/visualize-hyperparameter documentation built on Feb. 13, 2022, 8:11 a.m.