nestedcv (version 0.9.0)

train_summary: Summarise performance on outer training folds

Description

Calculates performance metrics on outer training folds: confusion matrix, accuracy and balanced accuracy for classification; ROC AUC for binary classification; RMSE, R^2 and mean absolute error (MAE) for regression.

Usage

train_summary(x)

Value

Returns performance metrics from outer training folds, see predSummary

Arguments

x

a nestcv.glmnet, nestcv.train or outercv object

Details

Note: the argument outer_train_predict must be set to TRUE in the original call to either nestcv.glmnet, nestcv.train or outercv.

See Also

predSummary

Examples

# \donttest{
data(iris)
x <- iris[, 1:4]
y <- iris[, 5]

fit <- nestcv.glmnet(y, x,
                     family = "multinomial",
                     alpha = 1,
                     outer_train_predict = TRUE,
                     n_outer_folds = 3)
summary(fit)
innercv_summary(fit)
train_summary(fit)

fit2 <- nestcv.train(y, x,
                    model="svm",
                    outer_train_predict = TRUE,
                    n_outer_folds = 3,
                    cv.cores = 2)
summary(fit2)
innercv_summary(fit2)
train_summary(fit2)
# }