self_evaluate_model: Create a confusion matrix and ROC of a model against its...

In elsayed-lab/hpgltools: A pile of (hopefully) useful R functions

Create a confusion matrix and ROC of a model against its training data. (and test data if the annotations are known)

Description

This assumes a set of partitions from create_partitions() which keeps the training metadata alongside the matrix of model variables. When available, that function also keeps the known annotations of the testing data. Given those annotations and the model created/tested from them, this runs confusionMatrix and ROC, collects the results, and provides them as a list.

Usage

self_evaluate_model(predictions, datasets, which_partition = 1, type = "train")

Arguments

predictions	Model created by train()
datasets	Set of training/testing partitions along with associated metadata annotations.
which_partition	Choose a paritiont to evaluate
type	Use the training or testing data?

elsayed-lab/hpgltools documentation built on May 9, 2024, 5:02 a.m.