smile.classification

Class RandomForest

java.lang.Object

smile.classification.RandomForest

All Implemented Interfaces:

java.io.Serializable, Classifier<double[]>, SoftClassifier<double[]>

public class RandomForest
extends java.lang.Object
implements SoftClassifier<double[]>, java.io.Serializable

Random forest for classification. Random forest is an ensemble classifier that consists of many decision trees and outputs the majority vote of individual trees. The method combines bagging idea and the random selection of features.

Each tree is constructed using the following algorithm:

1. If the number of cases in the training set is N, randomly sample N cases with replacement from the original data. This sample will be the training set for growing the tree.
2. If there are M input variables, a number m << M is specified such that at each node, m variables are selected at random out of the M and the best split on these m is used to split the node. The value of m is held constant during the forest growing.
3. Each tree is grown to the largest extent possible. There is no pruning.

The advantages of random forest are:

• For many data sets, it produces a highly accurate classifier.
• It runs efficiently on large data sets.
• It can handle thousands of input variables without variable deletion.
• It gives estimates of what variables are important in the classification.
• It generates an internal unbiased estimate of the generalization error as the forest building progresses.
• It has an effective method for estimating missing data and maintains accuracy when a large proportion of the data are missing.

The disadvantages are

• Random forests are prone to over-fitting for some datasets. This is even more pronounced on noisy data.
• For data including categorical variables with different number of levels, random forests are biased in favor of those attributes with more levels. Therefore, the variable importance scores from random forest are not reliable for this type of data.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	RandomForest.Trainer Trainer for random forest classifiers.

Constructor Summary

Constructors

Constructor and Description
RandomForest(Attribute[] attributes, double[][] x, int[] y, int ntrees) Constructor.
RandomForest(Attribute[] attributes, double[][] x, int[] y, int ntrees, int mtry) Constructor.
RandomForest(Attribute[] attributes, double[][] x, int[] y, int ntrees, int maxNodes, int nodeSize, int mtry, double subsample) Constructor.
RandomForest(Attribute[] attributes, double[][] x, int[] y, int ntrees, int maxNodes, int nodeSize, int mtry, double subsample, DecisionTree.SplitRule rule) Constructor.
RandomForest(Attribute[] attributes, double[][] x, int[] y, int ntrees, int maxNodes, int nodeSize, int mtry, double subsample, DecisionTree.SplitRule rule, int[] classWeight) Constructor.
RandomForest(double[][] x, int[] y, int ntrees) Constructor.
RandomForest(double[][] x, int[] y, int ntrees, int mtry) Constructor.

Method Summary

Modifier and Type	Method and Description
double	error() Returns the out-of-bag estimation of error rate.
DecisionTree[]	getTrees() Returns the decision trees.
double[]	importance() Returns the variable importance.
int	predict(double[] x) Predicts the class label of an instance.
int	predict(double[] x, double[] posteriori) Predicts the class label of an instance and also calculate a posteriori probabilities.
int	size() Returns the number of trees in the model.
double[]	test(double[][] x, int[] y) Test the model on a validation dataset.
double[][]	test(double[][] x, int[] y, ClassificationMeasure[] measures) Test the model on a validation dataset.
void	trim(int ntrees) Trims the tree model set to a smaller size in case of over-fitting.

Methods inherited from class java.lang.Object

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

Methods inherited from interface smile.classification.Classifier

predict

Constructor Detail

RandomForest

public RandomForest(double[][] x,
                    int[] y,
                    int ntrees)

Constructor. Learns a random forest for classification.

Parameters:

x - the training instances.

y - the response variable.

ntrees - the number of trees.

RandomForest

public RandomForest(double[][] x,
                    int[] y,
                    int ntrees,
                    int mtry)

Constructor. Learns a random forest for classification.

Parameters:

x - the training instances.

y - the response variable.

ntrees - the number of trees.

mtry - the number of random selected features to be used to determine the decision at a node of the tree. floor(sqrt(dim)) seems to give generally good performance, where dim is the number of variables.

RandomForest

public RandomForest(Attribute[] attributes,
                    double[][] x,
                    int[] y,
                    int ntrees)

Constructor. Learns a random forest for classification.

Parameters:

attributes - the attribute properties.

x - the training instances.

y - the response variable.

ntrees - the number of trees.

RandomForest

public RandomForest(Attribute[] attributes,
                    double[][] x,
                    int[] y,
                    int ntrees,
                    int mtry)

Constructor. Learns a random forest for classification.

Parameters:

attributes - the attribute properties.

x - the training instances.

y - the response variable.

ntrees - the number of trees.

mtry - the number of random selected features to be used to determine the decision at a node of the tree. floor(sqrt(dim)) seems to give generally good performance, where dim is the number of variables.

RandomForest

public RandomForest(Attribute[] attributes,
                    double[][] x,
                    int[] y,
                    int ntrees,
                    int maxNodes,
                    int nodeSize,
                    int mtry,
                    double subsample)

Constructor. Learns a random forest for classification.

Parameters:

attributes - the attribute properties.

x - the training instances.

y - the response variable.

ntrees - the number of trees.

mtry - the number of random selected features to be used to determine the decision at a node of the tree. floor(sqrt(dim)) seems to give generally good performance, where dim is the number of variables.

nodeSize - the minimum size of leaf nodes.

maxNodes - the maximum number of leaf nodes in the tree.

subsample - the sampling rate for training tree. 1.0 means sampling with replacement. < 1.0 means sampling without replacement.

RandomForest

public RandomForest(Attribute[] attributes,
                    double[][] x,
                    int[] y,
                    int ntrees,
                    int maxNodes,
                    int nodeSize,
                    int mtry,
                    double subsample,
                    DecisionTree.SplitRule rule)

Constructor. Learns a random forest for classification.

Parameters:

attributes - the attribute properties.

x - the training instances.

y - the response variable.

ntrees - the number of trees.

mtry - the number of random selected features to be used to determine the decision at a node of the tree. floor(sqrt(dim)) seems to give generally good performance, where dim is the number of variables.

nodeSize - the minimum size of leaf nodes.

maxNodes - the maximum number of leaf nodes in the tree.

subsample - the sampling rate for training tree. 1.0 means sampling with replacement. < 1.0 means sampling without replacement.

rule - Decision tree split rule.

RandomForest

public RandomForest(Attribute[] attributes,
                    double[][] x,
                    int[] y,
                    int ntrees,
                    int maxNodes,
                    int nodeSize,
                    int mtry,
                    double subsample,
                    DecisionTree.SplitRule rule,
                    int[] classWeight)

Constructor. Learns a random forest for classification.

Parameters:

attributes - the attribute properties.

x - the training instances.

y - the response variable.

ntrees - the number of trees.

mtry - the number of random selected features to be used to determine the decision at a node of the tree. floor(sqrt(dim)) seems to give generally good performance, where dim is the number of variables.

nodeSize - the minimum size of leaf nodes.

maxNodes - the maximum number of leaf nodes in the tree.

subsample - the sampling rate for training tree. 1.0 means sampling with replacement. < 1.0 means sampling without replacement.

rule - Decision tree split rule.

classWeight - Priors of the classes. The weight of each class is roughly the ratio of samples in each class. For example, if there are 400 positive samples and 100 negative samples, the classWeight should be [1, 4] (assuming label 0 is of negative, label 1 is of positive).

Method Detail

error

public double error()

Returns the out-of-bag estimation of error rate. The OOB estimate is quite accurate given that enough trees have been grown. Otherwise the OOB estimate can bias upward.

Returns:

the out-of-bag estimation of error rate

importance

public double[] importance()

Returns the variable importance. Every time a split of a node is made on variable the (GINI, information gain, etc.) impurity criterion for the two descendent nodes is less than the parent node. Adding up the decreases for each individual variable over all trees in the forest gives a fast measure of variable importance that is often very consistent with the permutation importance measure.

Returns:

the variable importance

size

public int size()

Returns the number of trees in the model.

Returns:

the number of trees in the model

trim

public void trim(int ntrees)

Trims the tree model set to a smaller size in case of over-fitting. Or if extra decision trees in the model don't improve the performance, we may remove them to reduce the model size and also improve the speed of prediction.

Parameters:

ntrees - the new (smaller) size of tree model set.

predict

public int predict(double[] x)

Description copied from interface: Classifier

Predicts the class label of an instance.

Specified by:

predict in interface Classifier<double[]>

Parameters:

x - the instance to be classified.

Returns:

the predicted class label.

predict

public int predict(double[] x,
                   double[] posteriori)

Description copied from interface: SoftClassifier

Predicts the class label of an instance and also calculate a posteriori probabilities. Classifiers may NOT support this method since not all classification algorithms are able to calculate such a posteriori probabilities.

Specified by:

predict in interface SoftClassifier<double[]>

Parameters:

x - the instance to be classified.

posteriori - the array to store a posteriori probabilities on output.

Returns:

the predicted class label

test

public double[] test(double[][] x,
                     int[] y)

Test the model on a validation dataset.

Parameters:

x - the test data set.

y - the test data response values.

Returns:

accuracies with first 1, 2, ..., decision trees.

test

public double[][] test(double[][] x,
                       int[] y,
                       ClassificationMeasure[] measures)

Test the model on a validation dataset.

Parameters:

x - the test data set.

y - the test data labels.

measures - the performance measures of classification.

Returns:

performance measures with first 1, 2, ..., decision trees.

getTrees

public DecisionTree[] getTrees()

Returns the decision trees.