rustyml 0.17.0

Machine learning models, neural networks, metrics, and data utilities in Rust
Documentation 
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//! Common machine learning estimators and their configuration types.
//!
//! The exports include estimator traits, distance metrics, solvers, and model parameter types.

pub use crate::machine_learning::{DistanceCalculationMetric, RegularizationType};
pub use crate::traits::{Fit, FitTransform, Predict, Transform};

pub use crate::machine_learning::KernelType;
pub use crate::machine_learning::clustering::{DBSCAN, KMeans, MeanShift, estimate_bandwidth};
pub use crate::machine_learning::decomposition::{EigenSolver, KernelPCA, PCA, SVDSolver};
pub use crate::machine_learning::discriminant_analysis::{DiscriminantSolver, LDA, Shrinkage};
pub use crate::machine_learning::ensemble::{Contamination, IsolationForest};
pub use crate::machine_learning::linear_model::{
    LeastSquaresSolver, LinearRegression, LogisticRegression, generate_polynomial_features,
};
pub use crate::machine_learning::manifold::{Init, TSNE, TSNEMethod};
pub use crate::machine_learning::neighbors::{KNN, WeightingStrategy};
pub use crate::machine_learning::svm::{LinearSVC, SVC};
pub use crate::machine_learning::tree::{Algorithm, DecisionTree, DecisionTreeParams};