autoplot.PredictionRegr: Plots for Regression Predictions

In mlr3viz: Visualizations for 'mlr3'

Plots for Regression Predictions

Description

Visualizations for mlr3::PredictionRegr. The argument type controls what kind of plot is drawn. Possible choices are:

• "xy" (default): Scatterplot of "true" response vs. "predicted" response. By default a linear model is fitted via geom_smooth(method = "lm") to visualize the trend between x and y (by default colored blue). In addition geom_abline() with slope = 1 is added to the plot. Note that geom_smooth() and geom_abline() may overlap, depending on the given data.

• "histogram": Histogram of residuals: r = y - \hat{y}.

• "residual": Plot of the residuals, with the response \hat{y} on the "x" and the residuals on the "y" axis. By default a linear model is fitted via geom_smooth(method = "lm") to visualize the trend between x and y (by default colored blue).

• ⁠"confidence⁠: Scatterplot of "true" response vs. "predicted" response with confidence intervals. Error bars calculated as object$reponse +- quantile * object$se and so only possible with predict_type = "se". geom_abline() with slope = 1 is added to the plot.

Usage

## S3 method for class 'PredictionRegr'
autoplot(
  object,
  type = "xy",
  binwidth = NULL,
  theme = theme_minimal(),
  quantile = 1.96,
  ...
)

Arguments

object	(mlr3::PredictionRegr).
type	(character(1)): Type of the plot. See description.
binwidth	(integer(1)) Width of the bins for the histogram.
theme	(ggplot2::theme()) The ggplot2::theme_minimal() is applied by default to all plots.
quantile	(numeric(1)) Quantile multiplier for standard errors for type="confidence". Default 1.96.
...	(ignored).

Value

ggplot2::ggplot().

Examples

if (requireNamespace("mlr3")) {
  library(mlr3)
  library(mlr3viz)

  task = tsk("boston_housing")
  learner = lrn("regr.rpart")
  object = learner$train(task)$predict(task)

  head(fortify(object))
  autoplot(object)
  autoplot(object, type = "histogram", binwidth = 1)
  autoplot(object, type = "residual")

 if (requireNamespace("mlr3learners")) {
  library(mlr3learners)
  learner = lrn("regr.ranger", predict_type = "se")
  object = learner$train(task)$predict(task)
  autoplot(object, type = "confidence")
 }
}

mlr3viz documentation built on Nov. 23, 2023, 5:07 p.m.