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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

predictProba(features: Array.Array<number>): Array.Array<number>

Make a probabilistic 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

predict(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.

Override:

Estimator#predict

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", or "normalized" (both returning the sigmoid of the dot product of the feature vector and unit-length weights)

Return:

Array<number>

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

public predictProba(features: Array.Array<number>): Array.Array<number> source

Make a probabilistic prediction for a data set.

Params:

NameTypeAttributeDescription
features Array.Array<number>

Features for each data point

Return:

Array.Array<number>

Probability predictions. Each array element contains the probability of the negative (0) class in the first element, and the probability of the positive (1) class in the second element

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:

*