Struct linfa_linear::TweedieRegressor

pub struct TweedieRegressor<A> {
    pub coef: Array1<A>,
    pub intercept: A,
    /* private fields */
}

Generalized Linear Model (GLM) with a Tweedie distribution

The Regressor can be used to model different GLMs depending on power, which determines the underlying distribution.

Power	Distribution
0	Normal
1	Poisson
(1, 2)	Compound Poisson Gamma
2	Gamma
3	Inverse Gaussian

NOTE: No distribution exists between 0 and 1

Learn more from sklearn’s excellent User Guide

Examples

Here’s an example on how to train a GLM on the diabetes dataset

use linfa::traits::{Fit, Predict};
use linfa_linear::TweedieRegressor;
use linfa::prelude::SingleTargetRegression;

let dataset = linfa_datasets::diabetes();
let model = TweedieRegressor::params().fit(&dataset).unwrap();
let pred = model.predict(&dataset);
let r2 = pred.r2(&dataset).unwrap();
println!("r2 from prediction: {}", r2);

Fields

coef: Array1<A>

Estimated coefficients for the linear predictor

intercept: A

Intercept or bias added to the linear model

Implementations

impl<F: Float> TweedieRegressor<F>

pub fn params() -> TweedieRegressorParams<F>

Trait Implementations

impl<A: Clone> Clone for TweedieRegressor<A>

fn clone(&self) -> TweedieRegressor<A>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<A: Debug> Debug for TweedieRegressor<A>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<A: PartialEq> PartialEq for TweedieRegressor<A>

fn eq(&self, other: &TweedieRegressor<A>) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<A: Float, D: Data<Elem = A>> PredictInplace<ArrayBase<D, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<A>, Dim<[usize; 1]>>> for TweedieRegressor<A>

fn predict_inplace(&self, x: &ArrayBase<D, Ix2>, y: &mut Array1<A>)

Predict the target

fn default_target(&self, x: &ArrayBase<D, Ix2>) -> Array1<A>

Create targets that predict_inplace works with.

impl<A> StructuralPartialEq for TweedieRegressor<A>