Expand description


linfa-datasets provides a collection of commonly used datasets ready to be used in tests and examples.

The Big Picture

linfa-datasets is a crate in the linfa ecosystem, an effort to create a toolkit for classical Machine Learning implemented in pure Rust, akin to Python’s scikit-learn.

Current State

Currently the following datasets are provided:

NameDescription#samples, #features, #targetsTargetsReference
irisThe Iris dataset provides samples of flower properties, belonging to three different classes. Only two of them are linearly separable. It was introduced by Ronald Fisher in 1936 as an example for linear discriminant analysis.150, 4, 1Multi-class classificationhere
winequalityThe winequality dataset measures different properties of wine, such as acidity, and gives a scoring from 3 to 8 in quality. It was collected in the north of Portugal.441, 10, 1Multi-class classificationhere
diabetesThe diabetes dataset gives samples of human biological measures, such as BMI, age, blood measures, and tries to predict the progression of diabetes.1599, 11, 1Regressionhere
linnerudThe linnerud dataset contains samples from 20 middle-aged men in a fitness club. Their physical capability, as well as biological measures are related.20, 3, 3Regressionhere

The purpose of this crate is to faciliate dataset loading and make it as simple as possible. Loaded datasets are returned as a linfa::Dataset structure with named features.

Additionally, this crate provides utility functions to randomly generate test datasets.

Using a dataset

To use one of the provided datasets in your project add the linfa-datasets crate to your Cargo.toml and enable the corresponding feature:

linfa-datasets = { version = "0.3.1", features = ["winequality"] }

You can then use the dataset in your working code:

let (train, valid) = linfa_datasets::winequality()