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import {BinaryLogisticRegression} from '@jsmlt/jsmlt/src/supervised/linear/logistic_regression.js'
public class | source

BinaryLogisticRegression

Extends:

Estimator → Classifier → BinaryLogisticRegression

Logistic Regression learner for binary classification problem.

Member Summary

Public Members
public

weights: *

public

Method Summary

Public Methods
public

Check whether training has convergence when using iterative training using trainIteration.
public

predict(features: Array.Array<number>, optionsUser: Object): Array<number>

Make a prediction for a data set.
public

train(X: *, y: *)

public

trainIteration(X: Array<Array<number>>, y: Array<mixed>): *

Train the classifier for a single iteration on the stored training data.

Inherited Summary

From class Estimator
public abstract

test(X: Array<Array<number>>): Array<mixed>

Make a prediction for a data set.
public abstract

train(X: Array<Array<number>>, y: Array<mixed>)

Train the supervised learning algorithm on a dataset.

Public Members

public weights: * source

public weightsIncrement: * source

Public Methods

public checkConvergence(): boolean source

Check whether training has convergence when using iterative training using trainIteration.

Return:

boolean

Whether the algorithm has converged

public predict(features: Array.Array<number>, optionsUser: Object): Array<number> source

Make a prediction for a data set.

Params:

NameTypeAttributeDescription
features Array.Array<number>

Features for each data point
optionsUser Object
  • optional

User-defined options
optionsUser.output string
  • optional
  • default: 'classLabels'

Output for predictions. Either "classLabels" (default, output predicted class label), "raw" (dot product of weights vector with augmented features vector) or "normalized" (dot product from "raw" but with unit-length weights)

Return:

Array<number>

Predictions. Output dependent on options.output, defaults to class labels

public train(X: *, y: *) source

Train the supervised learning algorithm on a dataset.

Override:

Estimator#train

Params:

NameTypeAttributeDescription
X *
y *

See:

public trainIteration(X: Array<Array<number>>, y: Array<mixed>): * source

Train the classifier for a single iteration on the stored training data.

Params:

NameTypeAttributeDescription
X Array<Array<number>>

Features per data point
y Array<mixed>

Class labels per data point

Return:

*