plot-methods: Model Performance Plots

In MachineShop: Machine Learning Models and Tools

Model Performance Plots

Description

Plot measures of model performance and predictor variable importance.

Usage

## S3 method for class 'Calibration'
plot(x, type = c("line", "point"), se = FALSE, ...)

## S3 method for class 'ConfusionList'
plot(x, ...)

## S3 method for class 'ConfusionMatrix'
plot(x, ...)

## S3 method for class 'LiftCurve'
plot(
  x,
  find = numeric(),
  diagonal = TRUE,
  stat = MachineShop::settings("stat.Curve"),
  ...
)

## S3 method for class 'MLModel'
plot(
  x,
  metrics = NULL,
  stat = MachineShop::settings("stat.TrainingParams"),
  type = c("boxplot", "density", "errorbar", "line", "violin"),
  ...
)

## S3 method for class 'PartialDependence'
plot(x, stats = NULL, ...)

## S3 method for class 'Performance'
plot(
  x,
  metrics = NULL,
  stat = MachineShop::settings("stat.Resample"),
  type = c("boxplot", "density", "errorbar", "violin"),
  ...
)

## S3 method for class 'PerformanceCurve'
plot(
  x,
  type = c("tradeoffs", "cutoffs"),
  diagonal = FALSE,
  stat = MachineShop::settings("stat.Curve"),
  ...
)

## S3 method for class 'Resample'
plot(
  x,
  metrics = NULL,
  stat = MachineShop::settings("stat.Resample"),
  type = c("boxplot", "density", "errorbar", "violin"),
  ...
)

## S3 method for class 'TrainingStep'
plot(
  x,
  metrics = NULL,
  stat = MachineShop::settings("stat.TrainingParams"),
  type = c("boxplot", "density", "errorbar", "line", "violin"),
  ...
)

## S3 method for class 'VariableImportance'
plot(x, n = Inf, ...)

Arguments

x	calibration, confusion, lift, trained model fit, partial dependence, performance, performance curve, resample, rfe, or variable importance result.
type	type of plot to construct.
se	logical indicating whether to include standard error bars.
...	arguments passed to other methods.
find	numeric true positive rate at which to display reference lines identifying the corresponding rates of positive predictions.
diagonal	logical indicating whether to include a diagonal reference line.
stat	function or character string naming a function to compute a summary statistic on resampled metrics for trained MLModel line plots and Resample model ordering. The original ordering is preserved if a value of NULL is given. For LiftCurve and PerformanceCurve classes, plots are of resampled metrics aggregated by the statistic if given or of resample-specific metrics if NULL.
metrics	vector of numeric indexes or character names of performance metrics to plot.
stats	vector of numeric indexes or character names of partial dependence summary statistics to plot.
n	number of most important variables to include in the plot.

Examples

## Requires prior installation of suggested package gbm to run

## Factor response example

fo <- Species ~ .
control <- CVControl()

gbm_fit <- fit(fo, data = iris, model = GBMModel, control = control)
plot(varimp(gbm_fit))

gbm_res1 <- resample(fo, iris, GBMModel(n.trees = 25), control)
gbm_res2 <- resample(fo, iris, GBMModel(n.trees = 50), control)
gbm_res3 <- resample(fo, iris, GBMModel(n.trees = 100), control)
plot(gbm_res3)

res <- c(GBM1 = gbm_res1, GBM2 = gbm_res2, GBM3 = gbm_res3)
plot(res)

MachineShop documentation built on Sept. 11, 2024, 6:28 p.m.