plot {MachineShop} | R Documentation |
Plot measures of model performance and predictor variable importance.
## 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 = NULL, diagonal = TRUE, stat = MachineShop::settings("stat.Curve"), ... ) ## S3 method for class 'MLModel' plot( x, metrics = NULL, stat = MachineShop::settings("stat.train"), 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.Resamples"), 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 'Resamples' plot( x, metrics = NULL, stat = MachineShop::settings("stat.Resamples"), type = c("boxplot", "density", "errorbar", "violin"), ... ) ## S3 method for class 'VarImp' plot(x, n = NULL, ...)
x |
calibration, confusion, lift, trained model fit, partial dependence, performance, performance curve, 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. |
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 |
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]. |
## 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)