jsat.classifiers.trees

Class MDA

java.lang.Object

jsat.classifiers.trees.MDA

All Implemented Interfaces:

Serializable, TreeFeatureImportanceInference

public class MDA
extends Object
implements TreeFeatureImportanceInference

Mean Decrease in Accuracy (MDA) measures feature importance by applying the classifier for each feature, and corruption one feature at a time as each dataum its pushed through the tree. The importance of a feature is them measured as the percent change in the target score when that feature was corrupted.

This approach is based off of Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
MDA()

Method Summary

Modifier and Type	Method and Description
<Type extends DataSet> double[]	getImportanceStats(TreeLearner model, DataSet<Type> data)

Methods inherited from class java.lang.Object

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

Constructor Detail

MDA

public MDA()

Method Detail

getImportanceStats

public <Type extends DataSet> double[] getImportanceStats(TreeLearner model,
                                                          DataSet<Type> data)

Specified by:

getImportanceStats in interface TreeFeatureImportanceInference

Parameters:

model - the tree model to infer feature importance from

data - the dataset to use for importance inference. Should be either a Classification or Regression dataset, depending on the type of the model.

Returns:

a double array with one entry for each feature. Numeric features start first, followed by categorical features. Larger values indicate higher importance, and all values must be non-negative. Otherwise, no constraints are placed on the output of this function.