All functions

confusion_matrix()

Takes in a trained model with X and y values to produce a confusion matrix visual. If predicted_y array is passed in,other evaluation scoring metrics such as Recall, and precision will also be produced

model_comparison_table()

model_comparison_table Takes in training data, testing data, with the target as the last column and fitted models with meaningful names, then generates a model comparison table.

plot_roc()

creates a ROC plot

plot_train_valid_error()

plot train/validation errors vs. parameter values