Module rusty_machine::learning::logistic_reg

Logistic Regression module

Contains implemention of logistic regression using gradient descent optimization.

The regressor will automatically add the intercept term so you do not need to format the input matrices yourself.

Usage

use rusty_machine::learning::logistic_reg::LogisticRegressor;
use rusty_machine::learning::SupModel;
use rusty_machine::linalg::Matrix;
use rusty_machine::linalg::Vector;

let inputs = Matrix::new(4,1,vec![1.0,3.0,5.0,7.0]);
let targets = Vector::new(vec![0.,0.,1.,1.]);

let mut log_mod = LogisticRegressor::default();

// Train the model
log_mod.train(&inputs, &targets).unwrap();

// Now we'll predict a new point
let new_point = Matrix::new(1,1,vec![10.]);
let output = log_mod.predict(&new_point).unwrap();

// Hopefully we classified our new point correctly!
assert!(output[0] > 0.5, "Our classifier isn't very good!");

We could have been more specific about the learning of the model by using the new constructor instead. This allows us to provide a GradientDesc object with custom parameters.

Structs

BaseLogisticRegressor

The Base Logistic Regression model.

LogisticRegressor

Logistic Regression Model.