Package smile.classification

Class IsotonicRegressionScaling

java.lang.Object

smile.classification.IsotonicRegressionScaling

All Implemented Interfaces:

Serializable

public class IsotonicRegressionScaling
extends Object
implements Serializable

A method to calibrate decision function value to probability. Compared to Platt's scaling, this approach fits a piecewise-constant non-decreasing function instead of logistic regression.

References

1. Alexandru Niculescu-Mizil and Rich Caruana. Predicting Good Probabilities With Supervised Learning. ICML, 2005.

See Also:

• Serialized Form

Constructor Summary

Constructors

Constructor	Description
IsotonicRegressionScaling(double[] buckets, double[] prob)	Constructor.

Method Summary

Modifier and Type	Method	Description
static IsotonicRegressionScaling	fit(double[] scores, int[] y)	Trains the Isotonic Regression scaling.
double	predict(double y)	Returns the posterior probability estimate P(y = 1 | x).
String	toString()

Methods inherited from class java.lang.Object

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

Constructor Details

IsotonicRegressionScaling

public IsotonicRegressionScaling(double[] buckets,
 double[] prob)

Constructor.

Parameters:

buckets - the step-wise buckets of function values in ascending order.

prob - the probability of instances falling into the corresponding buckets.

Method Details

fit

public static IsotonicRegressionScaling fit(double[] scores,
 int[] y)

Trains the Isotonic Regression scaling.

Parameters:

scores - The predicted scores.

y - The training labels.

Returns:

the model.

predict

public double predict(double y)

Returns the posterior probability estimate P(y = 1 | x).

Parameters:

y - the binary classifier output score.

Returns:

the estimated probability.

toString

public String toString()

Overrides:

toString in class Object