Crate dataset_ml 

Built-in dataset implementations for machine learning.

dataset-ml provides ready-to-use loaders for classic ML datasets built on top of dataset_core::Dataset. Each module is a worked example showing how to wrap Dataset<T> for a concrete data source: downloading from a URL, verifying a SHA-256 hash, parsing CSV records, and exposing typed accessors backed by ndarray.

Datasets

Module	Samples	Features	Task Type
iris	150	4	Classification
boston_housing	506	13	Regression
diabetes	768	8	Classification
titanic	891	11	Classification
wine_quality::red_wine_quality	1,599	11	Regression
wine_quality::white_wine_quality	4,898	11	Regression

Example

use dataset_ml::iris::Iris;

let iris = Iris::new("./data");
let (features, labels) = iris.data().unwrap();
assert_eq!(features.shape(), &[150, 4]);

All loaders are lazy: the first call downloads and parses the file, every subsequent call returns a cached reference. See the individual module docs for features, target, sample count, and source.

Re-exports

pub use boston_housing::BostonHousing;

pub use diabetes::Diabetes;

pub use iris::Iris;

pub use titanic::Titanic;

pub use wine_quality::red_wine_quality::RedWineQuality;

pub use wine_quality::white_wine_quality::WhiteWineQuality;

Modules

boston_housing

Boston Housing dataset module.

diabetes

Diabetes dataset module.

iris

Iris flower dataset module.

titanic

Titanic dataset module.

wine_quality

Wine Quality dataset module.