MachineShop (version 3.5.0)

plot: Model Performance Plots

Description

Plot measures of model performance and predictor variable importance.

Usage

# S3 method for Calibration
plot(x, type = c("line", "point"), se = FALSE, ...)
# S3 method for ConfusionList
plot(x, ...)
# S3 method for ConfusionMatrix
plot(x, ...)
# S3 method for LiftCurve
plot(
  x,
  find = numeric(),
  diagonal = TRUE,
  stat = MachineShop::settings("stat.Curve"),
  ...
)
# S3 method for MLModel
plot(
  x,
  metrics = NULL,
  stat = MachineShop::settings("stat.TrainingParams"),
  type = c("boxplot", "density", "errorbar", "line", "violin"),
  ...
)
# S3 method for PartialDependence
plot(x, stats = NULL, ...)
# S3 method for Performance
plot(
  x,
  metrics = NULL,
  stat = MachineShop::settings("stat.Resample"),
  type = c("boxplot", "density", "errorbar", "violin"),
  ...
)
# S3 method for PerformanceCurve
plot(
  x,
  type = c("tradeoffs", "cutoffs"),
  diagonal = FALSE,
  stat = MachineShop::settings("stat.Curve"),
  ...
)
# S3 method for Resample
plot(
  x,
  metrics = NULL,
  stat = MachineShop::settings("stat.Resample"),
  type = c("boxplot", "density", "errorbar", "violin"),
  ...
)
# S3 method for TrainingStep
plot(
  x,
  metrics = NULL,
  stat = MachineShop::settings("stat.TrainingParams"),
  type = c("boxplot", "density", "errorbar", "line", "violin"),
  ...
)
# S3 method for 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

# \donttest{
## 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)
# }