Package ai.sklearn4j.base

Class ClassifierMixin

java.lang.Object

ai.sklearn4j.base.ClassifierMixin

Direct Known Subclasses:

BaseNaiveBayes

public abstract class ClassifierMixin
extends Object

Mixin class for all classifiers in scikit-learn.

Constructor Summary

Constructors

Constructor	Description
ClassifierMixin()

Method Summary

Modifier and Type	Method	Description
abstract NumpyArray<Long>	predict(NumpyArray<Double> x)	Perform classification on an array of test vectors X.
abstract NumpyArray<Double>	predictLogProbabilities(NumpyArray<Double> x)	Return log-probability estimates for the test vector X.
abstract NumpyArray<Double>	predictProbabilities(NumpyArray<Double> x)	Return probability estimates for the test vector X.

Methods inherited from class java.lang.Object

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

Constructor Detail

ClassifierMixin

public ClassifierMixin()

Method Detail

predict

public abstract NumpyArray<Long> predict(NumpyArray<Double> x)

Perform classification on an array of test vectors X.

Parameters:

x - Array-like of shape (n_samples, n_features) The input samples.

Returns:

NumpyArray of shape (n_samples,) Predicted target values for X.

predictLogProbabilities

public abstract NumpyArray<Double> predictLogProbabilities(NumpyArray<Double> x)

Return log-probability estimates for the test vector X.

Parameters:

x - array-like of shape (n_samples, n_features) The input samples.

Returns:

array-like of shape (n_samples, n_classes) Returns the log-probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute :term:`classes_`.

predictProbabilities

public abstract NumpyArray<Double> predictProbabilities(NumpyArray<Double> x)

Return probability estimates for the test vector X.

Parameters:

x - array-like of shape (n_samples, n_features) The input samples.

Returns:

array-like of shape (n_samples, n_classes) Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute :term:`classes_`.