sklears 0.1.0-beta.1

A comprehensive machine learning library in Rust, inspired by scikit-learn
Documentation

sklears

There is very little structured metadata to build this page from currently. You should check the main library docs, readme, or Cargo.toml in case the author documented the features in them.

This version has 72 feature flags, 11 of them enabled by default.

default

clustering (default)

linear (default)

metrics (default)

model-selection (default)

neighbors (default)

std (default)

sklears-clustering (default)

sklears-linear (default)

sklears-metrics (default)

sklears-model-selection (default)

sklears-neighbors (default)

all-algorithms

backend-blas

This feature flag does not enable additional features.

backend-cpu

This feature flag does not enable additional features.

backend-cuda

This feature flag does not enable additional features.

backend-wgpu

This feature flag does not enable additional features.

bench

calibration

compose

covariance

criterion

cross-decomposition

datasets

decomposition

dev

discriminant-analysis

dummy

ensemble

feature-extraction

feature-selection

gaussian-process

impute

inspection

isotonic

kernel-approximation

manifold

mixture

multiclass

multioutput

naive-bayes

neural

parallel

rayon

semi-supervised

serde

sklears-calibration

sklears-compose

sklears-covariance

sklears-cross-decomposition

sklears-datasets

sklears-decomposition

sklears-discriminant-analysis

sklears-dummy

sklears-ensemble

sklears-feature-extraction

sklears-feature-selection

sklears-gaussian-process

sklears-impute

sklears-inspection

sklears-isotonic

sklears-kernel-approximation

sklears-manifold

sklears-mixture

sklears-multiclass

sklears-multioutput

sklears-naive-bayes

sklears-neural

sklears-semi-supervised

sklears-svm

sklears-tree

svm

tree