public class LogisticRegression extends Object implements Classifier<double[]>
Goodness-of-fit tests such as the likelihood ratio test are available as indicators of model appropriateness, as is the Wald statistic to test the significance of individual independent variables.
Logistic regression has many analogies to ordinary least squares (OLS) regression. Unlike OLS regression, however, logistic regression does not assume linearity of relationship between the raw values of the independent variables and the dependent, does not require normally distributed variables, does not assume homoscedasticity, and in general has less stringent requirements.
Compared with linear discriminant analysis, logistic regression has several advantages:
Logistic regression also has strong connections with neural network and maximum entropy modeling. For example, binary logistic regression is equivalent to a one-layer, single-output neural network with a logistic activation function trained under log loss. Similarly, multinomial logistic regression is equivalent to a one-layer, softmax-output neural network.
Logistic regression estimation also obeys the maximum entropy principle, and thus logistic regression is sometimes called "maximum entropy modeling", and the resulting classifier the "maximum entropy classifier".
NeuralNetwork,
Maxent,
LDA| Modifier and Type | Class and Description |
|---|---|
static class |
LogisticRegression.Trainer
Trainer for logistic regression.
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| Constructor and Description |
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LogisticRegression(double[][] x,
int[] y)
Constructor.
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LogisticRegression(double[][] x,
int[] y,
double lambda)
Constructor.
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LogisticRegression(double[][] x,
int[] y,
double lambda,
double tol,
int maxIter)
Constructor.
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| Modifier and Type | Method and Description |
|---|---|
double |
loglikelihood()
Returns the log-likelihood of model.
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int |
predict(double[] x)
Predicts the class label of an instance.
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int |
predict(double[] x,
double[] posteriori)
Predicts the class label of an instance and also calculate a posteriori
probabilities.
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public LogisticRegression(double[][] x,
int[] y)
x - training samples.y - training labels in [0, k), where k is the number of classes.public LogisticRegression(double[][] x,
int[] y,
double lambda)
x - training samples.y - training labels in [0, k), where k is the number of classes.lambda - λ > 0 gives a "regularized" estimate of linear
weights which often has superior generalization performance, especially
when the dimensionality is high.public LogisticRegression(double[][] x,
int[] y,
double lambda,
double tol,
int maxIter)
x - training samples.y - training labels in [0, k), where k is the number of classes.lambda - λ > 0 gives a "regularized" estimate of linear
weights which often has superior generalization performance, especially
when the dimensionality is high.tol - the tolerance for stopping iterations.maxIter - the maximum number of iterations.public double loglikelihood()
public int predict(double[] x)
Classifierpredict in interface Classifier<double[]>x - the instance to be classified.public int predict(double[] x,
double[] posteriori)
Classifierpredict in interface Classifier<double[]>x - the instance to be classified.posteriori - the array to store a posteriori probabilities on output.Copyright © 2015. All rights reserved.