plot_error | R Documentation |
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 |
The plot_model provides a visualization of the models performance on the testing paritions for the train_model function output
A plot with a summery of the models error rate by testing partition
## 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)
## End(Not run)
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