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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, Ranks Features, Verbose, Persistable

Data Type Compatibility: Continuous


# Name Default Type Description
1 batchSize 128 int The number of training samples to process at a time.
2 optimizer Adam Optimizer The gradient descent optimizer used to update the network parameters.
3 l2Penalty 1e-4 float The amount of L2 regularization applied to the weights of the output layer.
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 minChange 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 costFn CrossEntropy ClassificationLoss The function that computes the loss associated with an erroneous activation during training.


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

$estimator = new LogisticRegression(64, new Adam(0.001), 1e-4, 100, 1e-4, 5, new CrossEntropy());

Additional Methods#

Return an iterable progress table with the steps from the last training session:

public steps() : iterable

use Rubix\ML\Extractors\CSV;

$extractor = new CSV('progress.csv', true);


Return the loss for each epoch from the last training session:

public losses() : float[]|null

Return the underlying neural network instance or null if untrained:

public network() : Network|null