smile.classification

Class LogisticRegression.Trainer

java.lang.Object

smile.classification.ClassifierTrainer<double[]>

smile.classification.LogisticRegression.Trainer

Enclosing class:

LogisticRegression

public static class LogisticRegression.Trainer
extends ClassifierTrainer<double[]>

Trainer for logistic regression.

Constructor Summary

Constructors

Constructor and Description
LogisticRegression.Trainer() Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	setMaxNumIteration(int maxIter) Sets the maximum number of iterations.
void	setRegularizationFactor(double lambda) Sets the regularization factor.
void	setTolerance(double tol) Sets the tolerance for BFGS stopping iterations.
LogisticRegression	train(double[][] x, int[] y) Learns a classifier with given training data.

Methods inherited from class smile.classification.ClassifierTrainer

setAttributes

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

LogisticRegression.Trainer

public LogisticRegression.Trainer()

Constructor.

Method Detail

setRegularizationFactor

public void setRegularizationFactor(double lambda)

Sets the regularization factor. λ > 0 gives a "regularized" estimate of linear weights which often has superior generalization performance, especially when the dimensionality is high.

Parameters:

lambda - regularization factor.

setTolerance

public void setTolerance(double tol)

Sets the tolerance for BFGS stopping iterations.

Parameters:

tol - the tolerance for stopping iterations.

setMaxNumIteration

public void setMaxNumIteration(int maxIter)

Sets the maximum number of iterations.

Parameters:

maxIter - the maximum number of iterations.

train

public LogisticRegression train(double[][] x,
                       int[] y)

Description copied from class: ClassifierTrainer

Learns a classifier with given training data.

Specified by:

train in class ClassifierTrainer<double[]>

Parameters:

x - the training instances.

y - the training labels.

Returns:

a trained classifier.