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Logistic Regression#

A linear classifier that uses the logistic (sigmoid) function to estimate the probabilities of exactly two class outcomes. The model parameters (weights and bias) are solved using mini batch Gradient Descent with pluggable optimizers and cost functions that run on the neural network subsystem.

Interfaces: Estimator, Learner, Online, Probabilistic, Verbose, Persistable

Data Type Compatibility: Continuous

Parameters#

# Param Default Type Description
1 batch size 100 int The number of training samples to process at a time.
2 optimizer Adam object The gradient descent optimizer used to update the network parameters.
3 alpha 1e-4 float The amount of L2 regularization to apply to the parameters of the network.
4 epochs 1000 int The maximum number of training epochs. i.e. the number of times to iterate over the entire training set before terminating.
5 min change 1e-4 float The minimum change in the training loss necessary to continue training.
6 window 5 int The number of epochs without improvement in the training loss to wait before considering an early stop.
7 cost fn CrossEntropy object The function that computes the loss associated with an erroneous activation during training.

Additional Methods#

Return the training loss at each epoch:

public steps() : array

Return the underlying neural network instance or null if untrained:

public network() : Network|null

Example#

use Rubix\ML\Classifiers\LogisticRegression;
use Rubix\ML\NeuralNet\Optimizers\Adam;
use Rubix\ML\NeuralNet\CostFunctions\CrossEntropy;

$estimator = new LogisticRegression(10, new Adam(0.001), 1e-4, 100, 1e-4, 5, new CrossEntropy());