autoplot.PredictionRegr: Plot for PredictionRegr

In mlr3viz: Visualizations for 'mlr3'

Description

Generates plots for mlr3::PredictionRegr, depending on argument type:

• "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 - y.hat.

• "residual": Plot of the residuals, with the response y.hat 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).

Usage

## S3 method for class 'PredictionRegr'
autoplot(object, type = "xy", ...)

Arguments

object	(mlr3::PredictionRegr).
type	(character(1)): Type of the plot. See description.
...	(any): Additional arguments, passed down to the respective geom.

Value

ggplot2::ggplot() object.

Examples

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")

Example output

   row_id truth response
1:      1  24.0 23.72519
2:      2  21.6 19.55954
3:      3  34.7 35.10313
4:      4  33.4 35.10313
5:      5  36.2 35.10313
6:      6  28.7 29.98333
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

mlr3viz documentation built on July 2, 2021, 1:07 a.m.