PlatypusML_balance: Secondary ML for crossvalidation

In Platypus: Single-Cell Immune Repertoire and Gene Expression Analysis

Secondary ML for crossvalidation

Description

This PlatypusML_classification function takes as input encoded features obtained using the PlatypusML_extract_features function. The function runs cross validation on a specified number of folds for different classification models and reports the AUC scores and ROC curves.

Usage

PlatypusML_balance(matrix, label.1, label.2, proportion, random.seed)

Arguments

matrix	Matrix. Output of the PlatypusML_extract_features function, with the last column storing the label.
label.1	String. The label of the first class.
label.2	String. The label of the second class.
proportion	Vector of size 2 (floats between 0 and 1 that need to sum up to 1). Specifies the proportions for the two classes. The smaller proportion will be assigned to the minority class by default.
random.seed	Integer. The seed to be set when sampling for balancing the dataset.

Value

This function returns a matrix containing equal number of samples for the two classes.

Examples

## Not run: 
TODO: example

## End(Not run)

Platypus documentation built on Aug. 15, 2022, 9:08 a.m.