sklears-multioutput 0.1.1

Multi-output regression and classification
Documentation 
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//! Instance-based learning algorithms for multi-output prediction
//!
//! This module provides instance-based learning algorithms that make predictions
//! based on similarity to training instances.

// Use SciRS2-Core for arrays and random number generation (SciRS2 Policy)
use scirs2_core::ndarray::{Array1, Array2, Axis};
use sklears_core::{
    error::{Result as SklResult, SklearsError},
    traits::{Estimator, Fit, Predict, Transform, Untrained},
    types::Float,
};
use crate::multi_label::{BinaryRelevance, BinaryRelevanceTrained};
use crate::utils::*;
use std::collections::{HashMap, VecDeque};
use std::fmt;

// Placeholder for now - will add algorithms here