train_summary: Summarise performance on outer training folds

In nestedcv: Nested Cross-Validation with 'glmnet' and 'caret'

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)

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.

Value

Returns performance metrics from outer training folds, see predSummary

See Also

predSummary

Examples

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)

nestedcv documentation built on Oct. 26, 2023, 5:08 p.m.