@PublicationReference(title="Bagging Predictors", author="Leo Breiman", year=1996, type=Journal, publication="Machine Learning", pages={123,140}, url="http://www.springerlink.com/index/L4780124W2874025.pdf") public class RandomForestFactory extends AbstractCloneableSerializable
Constructor and Description |
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RandomForestFactory() |
Modifier and Type | Method and Description |
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static <CategoryType> |
createCategorizationLearner(int ensembleSize,
double baggingFraction,
double dimensionsFraction,
int maxTreeDepth,
int minLeafSize,
java.util.Random random)
Creates a random forest learner for categorization outputs.
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static <CategoryType> |
createRegressionLearner(int ensembleSize,
double baggingFraction,
double dimensionsFraction,
int maxTreeDepth,
int minLeafSize,
java.util.Random random)
Creates a random forest learner for categorization outputs.
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clone
public static <CategoryType> BaggingCategorizerLearner<Vector,CategoryType> createCategorizationLearner(int ensembleSize, double baggingFraction, double dimensionsFraction, int maxTreeDepth, int minLeafSize, java.util.Random random)
CategoryType
- The type of categories.ensembleSize
- The size of the ensemble to learn. Must be non-negative.baggingFraction
- The percentage of the data to sample (with replacement) to train
each ensemble member.dimensionsFraction
- The percentage of the dimensions to sample at each node in each
tree when training in order to determine the best split point.maxTreeDepth
- The maximum allowed tree depth. Must be positive.minLeafSize
- The minimum allowed number of examples that are allowed to fall
into a leaf.random
- The random number generator to use.public static <CategoryType> BaggingRegressionLearner<Vector> createRegressionLearner(int ensembleSize, double baggingFraction, double dimensionsFraction, int maxTreeDepth, int minLeafSize, java.util.Random random)
CategoryType
- The type of categories.ensembleSize
- The size of the ensemble to learn. Must be non-negative.baggingFraction
- The percentage of the data to sample (with replacement) to train
each ensemble member.dimensionsFraction
- The percentage of the dimensions to sample at each node in each
tree when training in order to determine the best split point.maxTreeDepth
- The maximum allowed tree depth. Must be positive.minLeafSize
- The minimum allowed number of examples that are allowed to fall
into a leaf.random
- The random number generator to use.