Plot prediction diagnostics for a fitted neural network

Produces diagnostic plots comparing fitted values against actual (true) response values for a fitted nn_fit model.

• Regression (single output): returns a named list with two panels – residuals vs fitted and actual vs fitted.

• Regression (multi-output): returns a named list with one actual vs fitted panel per output column.

• Classification: returns a single confusion matrix heatmap.

Usage

autoplot_diagnostics(object, actual, ...)

plot_diagnostics(object, actual, ...)

Arguments

object

A fitted model of class nn_fit, as returned by train_nn(), ffnn(), or rnn().

actual

A vector of true response values, the same length as the training data used to fit object.

...

Additional arguments (currently unused).

Value

For regression, a named list of ggplot2::ggplot() objects (one per diagnostic panel). For classification, a single ggplot2::ggplot() confusion matrix heatmap.

Examples

# \donttest{
  if (torch::torch_is_installed()) {
    # Regression
    m = train_nn(
      as.matrix(iris[, 2:4]), iris$Sepal.Length,
      epochs = 5
    )
    autoplot_diagnostics(m, actual = iris$Sepal.Length)

    # Classification
    m_cls = train_nn(
      as.matrix(iris[, 1:4]), iris$Species,
      epochs = 5
    )
    autoplot_diagnostics(m_cls, actual = iris$Species)
  }
#> Returning two plots. Install patchwork and use `p[[1]] + p[[2]]` to combine
#> them.

# }