train_test_plot: Creates plot of training and test error for trained model.
In UBC-MDS/ezmodelR:

Description Usage Arguments Value Examples

Creates plot of training and test error for trained model.

1 2	train_test_plot(model, score_type, x, y, hyperparameter, param_range, random_seed)

`model:`	Currently only works with argument 'decision_tree'. Generally: String specifying decision_tree, lasso, ridge regression, or logistic regression. Argument should be one of "decision_tree", "lasso", "ridge", or "logistic"..
`score_type:`	(list or str): Should be one of (mse, r2, adj_r2, auc, ...). If a vector, then a vector containing several of those entries as elements
`x:`	n x d dataframe containing features
`y:`	n x 1 dataframe containing response values.
`hyperparameter:`	string defining hyperparameter to iterate over
`param_range:`	vector of hyperparameter values to iterate over
`random_seed:`	Default = None. If set to integer, defines the random train_test_split

ggplot object showing training and test score vs. hyperparameter values.

## Not run: data('Sonar')
## Not run: X <- Sonar[,1:60]
## Not run: Y <- Sonar[,61]

## Not run: train_test_plot(model = "decision_tree", score_type = "accuracy", x = X,
  y = Y, hyperparameter = "cp", param_range = c(0.1,0.2,0.3), random_seed=123)
## End(Not run)