mi_balance_data: Data balance. Most classes adopt random undersampling, while...

In MantaID: A Machine-Learning Based Tool to Automate the Identification of Biological Database IDs

Data balance. Most classes adopt random undersampling, while a few classes adopt smote method to oversample to obtain relatively balanced data.

Description

Data balance. Most classes adopt random undersampling, while a few classes adopt smote method to oversample to obtain relatively balanced data.

Usage

mi_balance_data(data, ratio = 0.3, parallel = FALSE)

Arguments

data	A data frame. Except for class column, all are numeric types.
ratio	Numeric between 0 and 1. The percent of the test set split from data.
parallel	Logical.

Value

A list contains a train set and a test set.

MantaID documentation built on Sept. 11, 2024, 6 p.m.