Plot the Models Error Rates on the Testing Partitions
plot_error(model.obj, error = "MAPE", palette = "Set1")
| model.obj | A train_model object |
|---|---|
| error | A character, defines the type of error metrics to plot, possible metric - "MAPE" or "RMSE" |
| palette | A character, defines the color type to used on the plot, use row.names(RColorBrewer::brewer.pal.info) to view possible color palletes |
A plot with a summery of the models error rate by testing partition
The plot_model provides a visualization of the models performance on the testing paritions for the train_model function output
# NOT RUN { # Defining the models and their arguments methods <- list(ets1 = list(method = "ets", method_arg = list(opt.crit = "lik"), notes = "ETS model with opt.crit = lik"), ets2 = list(method = "ets", method_arg = list(opt.crit = "amse"), notes = "ETS model with opt.crit = amse"), arima1 = list(method = "arima", method_arg = list(order = c(2,1,0)), notes = "ARIMA(2,1,0)"), arima2 = list(method = "arima", method_arg = list(order = c(2,1,2), seasonal = list(order = c(1,1,1))), notes = "SARIMA(2,1,2)(1,1,1)"), hw = list(method = "HoltWinters", method_arg = NULL, notes = "HoltWinters Model"), tslm = list(method = "tslm", method_arg = list(formula = input ~ trend + season), notes = "tslm model with trend and seasonal components")) # Training the models with backtesting md <- train_model(input = USgas, methods = methods, train_method = list(partitions = 6, sample.out = 12, space = 3), horizon = 12, error = "MAPE") # Plot the models performance on the testing partitions plot_error(model.obj = md) # }