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BreakNBuild: Optimize Machine Learning Models with Dynamic Data Splits

Overview

BreakNBuild is an R package designed to evaluate model performance with progressively sampled data. This approach is particularly useful for debugging in machine learning, as it allows you to observe the bias-variance trade-off in relation to the sample size used for training the model.

Features

  • Progressive Data Splitting: partition your dataset into training and validation subsets.
  • Customizable Sample Sizes: Control the size of your training data to understand model performance under different conditions.
  • Easy Integration: Built on the rsample package, BreakNBuild seamlessly integrates with the tidymodels framework.

[man/figures/schema_progressive_split.svg]

Installation

To install the latest version from GitHub, use:

# install.packages("devtools")
devtools::install_github("https://github.com/focardozom/BreakNBuild")

Usage

Here’s a quick example to get you started:

library(BreakNBuild)

splits <- progressive_splits(data, validation_size = 0.2, start_size = 10)