MachineShop (version 2.0.0)

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 Curves
plot(
  x,
  type = c("tradeoffs", "cutoffs"),
  diagonal = FALSE,
  stat = MachineShop::settings("stat.Curves"),
  ...
)
# S3 method for Lift
plot(
  x,
  find = NULL,
  diagonal = TRUE,
  stat = MachineShop::settings("stat.Curves"),
  ...
)
# S3 method for MLModel
plot(
  x,
  metrics = NULL,
  stat = MachineShop::settings("stat.Train"),
  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.Resamples"),
  type = c("boxplot", "density", "errorbar", "violin"),
  ...
)
# S3 method for Resamples
plot(
  x,
  metrics = NULL,
  stat = MachineShop::settings("stat.Resamples"),
  type = c("boxplot", "density", "errorbar", "violin"),
  ...
)
# S3 method for VarImp
plot(x, n = NULL, ...)

Arguments

x

calibration, confusion, performance curve, lift, trained model fit, partial dependence, performance, resample, or variable importance result.

type

type of plot to construct.

se

logical indicating whether to include standard error bars.

...

arguments passed to other methods.

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 Resamples model ordering. For Curves and Lift classes, plots are of resampled metrics aggregated by the statistic if given or of resample-specific metrics if NULL.

find

numeric true positive rate at which to display reference lines identifying the corresponding rates of positive predictions.

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 [default: all].

Examples

# NOT 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)

# }