Crate scry_learn 

scry-learn

Machine learning toolkit in pure Rust.

Quick Start

use scry_learn::prelude::*;

let data = Dataset::from_csv("iris.csv", "species")?;
let (train, test) = train_test_split(&data, 0.2, 42);

let mut model = RandomForestClassifier::new()
    .n_estimators(100)
    .max_depth(10);
model.fit(&train)?;

let preds = model.predict(&test)?;
let report = classification_report(&test.target, &preds);
println!("{report}");

Modules

anomaly

Anomaly detection algorithms.

calibration

Probability calibration for classifiers.

cluster

Clustering algorithms: K-Means, Mini-Batch K-Means, DBSCAN, HDBSCAN, Agglomerative Clustering, and silhouette scoring.

dataset

Tabular dataset container for ML workflows.

distance

Shared distance functions.

ensemble

Ensemble meta-learning methods.

error

Error types for scry-learn.

explain

Model explainability: permutation importance and TreeSHAP.

feature_selection

Feature selection transformers.

linear

Linear models: OLS, Ridge, Logistic, Lasso, and ElasticNet.

metrics

Classification, regression, and clustering metrics.

naive_bayes

Naive Bayes classifiers: Gaussian, Bernoulli, and Multinomial.

neighbors

K-Nearest Neighbors classifier and regressor.

neural

Neural network module — MLP and CNN layers.

partial_fit

Incremental (online) learning trait.

pipeline

Composable ML pipeline.

prelude

Convenience re-exports for common usage.

preprocess

Data preprocessing transformers.

search

Hyperparameter search via cross-validation.

sparse

Sparse matrix types: CSR (Compressed Sparse Row) and CSC (Compressed Sparse Column).

split

Train/test splitting and cross-validation utilities.

svm

Support Vector Machine classifiers and regressors.

text

Text processing and feature extraction for NLP tasks.

tree

Tree-based models: Decision Tree, Random Forest, Gradient Boosting, and Histogram-based Gradient Boosting.

weights

Class weighting for imbalanced datasets.