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Object org.apache.spark.mllib.tree.RandomForest
public class RandomForest
:: Experimental ::
A class that implements a Random Forest
learning algorithm for classification and regression.
It supports both continuous and categorical features.
The settings for featureSubsetStrategy are based on the following references: - log2: tested in Breiman (2001) - sqrt: recommended by Breiman manual for random forests - The defaults of sqrt (classification) and onethird (regression) match the R randomForest package.
http://www.stat.berkeley.edu/~breiman/Using_random_forests_V3.1.pdf Breiman manual for
random forests}
param: strategy The configuration parameters for the random forest algorithm which specify
the type of algorithm (classification, regression, etc.), feature type
(continuous, categorical), depth of the tree, quantile calculation strategy,
etc.
param: numTrees If 1, then no bootstrapping is used. If > 1, then bootstrapping is done.
param: featureSubsetStrategy Number of features to consider for splits at each node.
Supported: "auto", "all", "sqrt", "log2", "onethird".
If "auto" is set, this parameter is set based on numTrees:
if numTrees == 1, set to "all";
if numTrees > 1 (forest) set to "sqrt" for classification and
to "onethird" for regression.
param: seed Random seed for bootstrapping and choosing feature subsets.
,
Serialized FormConstructor Summary | |
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RandomForest(Strategy strategy,
int numTrees,
String featureSubsetStrategy,
int seed)
|
Method Summary | |
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RandomForestModel |
run(RDD<LabeledPoint> input)
Method to train a decision tree model over an RDD |
static String[] |
supportedFeatureSubsetStrategies()
List of supported feature subset sampling strategies. |
static RandomForestModel |
trainClassifier(JavaRDD<LabeledPoint> input,
int numClasses,
java.util.Map<Integer,Integer> categoricalFeaturesInfo,
int numTrees,
String featureSubsetStrategy,
String impurity,
int maxDepth,
int maxBins,
int seed)
Java-friendly API for RandomForest$.trainClassifier(org.apache.spark.rdd.RDD |
static RandomForestModel |
trainClassifier(RDD<LabeledPoint> input,
int numClasses,
scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo,
int numTrees,
String featureSubsetStrategy,
String impurity,
int maxDepth,
int maxBins,
int seed)
Method to train a decision tree model for binary or multiclass classification. |
static RandomForestModel |
trainClassifier(RDD<LabeledPoint> input,
Strategy strategy,
int numTrees,
String featureSubsetStrategy,
int seed)
Method to train a decision tree model for binary or multiclass classification. |
static RandomForestModel |
trainRegressor(JavaRDD<LabeledPoint> input,
java.util.Map<Integer,Integer> categoricalFeaturesInfo,
int numTrees,
String featureSubsetStrategy,
String impurity,
int maxDepth,
int maxBins,
int seed)
Java-friendly API for RandomForest$.trainRegressor(org.apache.spark.rdd.RDD |
static RandomForestModel |
trainRegressor(RDD<LabeledPoint> input,
scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo,
int numTrees,
String featureSubsetStrategy,
String impurity,
int maxDepth,
int maxBins,
int seed)
Method to train a decision tree model for regression. |
static RandomForestModel |
trainRegressor(RDD<LabeledPoint> input,
Strategy strategy,
int numTrees,
String featureSubsetStrategy,
int seed)
Method to train a decision tree model for regression. |
Methods inherited from class Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Methods inherited from interface org.apache.spark.Logging |
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initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning |
Constructor Detail |
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public RandomForest(Strategy strategy, int numTrees, String featureSubsetStrategy, int seed)
Method Detail |
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public static RandomForestModel trainClassifier(RDD<LabeledPoint> input, Strategy strategy, int numTrees, String featureSubsetStrategy, int seed)
input
- Training dataset: RDD of LabeledPoint
.
Labels should take values {0, 1, ..., numClasses-1}.strategy
- Parameters for training each tree in the forest.numTrees
- Number of trees in the random forest.featureSubsetStrategy
- Number of features to consider for splits at each node.
Supported: "auto", "all", "sqrt", "log2", "onethird".
If "auto" is set, this parameter is set based on numTrees:
if numTrees == 1, set to "all";
if numTrees > 1 (forest) set to "sqrt".seed
- Random seed for bootstrapping and choosing feature subsets.
public static RandomForestModel trainClassifier(RDD<LabeledPoint> input, int numClasses, scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo, int numTrees, String featureSubsetStrategy, String impurity, int maxDepth, int maxBins, int seed)
input
- Training dataset: RDD of LabeledPoint
.
Labels should take values {0, 1, ..., numClasses-1}.numClasses
- number of classes for classification.categoricalFeaturesInfo
- Map storing arity of categorical features.
E.g., an entry (n -> k) indicates that feature n is categorical
with k categories indexed from 0: {0, 1, ..., k-1}.numTrees
- Number of trees in the random forest.featureSubsetStrategy
- Number of features to consider for splits at each node.
Supported: "auto", "all", "sqrt", "log2", "onethird".
If "auto" is set, this parameter is set based on numTrees:
if numTrees == 1, set to "all";
if numTrees > 1 (forest) set to "sqrt".impurity
- Criterion used for information gain calculation.
Supported values: "gini" (recommended) or "entropy".maxDepth
- Maximum depth of the tree.
E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
(suggested value: 4)maxBins
- maximum number of bins used for splitting features
(suggested value: 100)seed
- Random seed for bootstrapping and choosing feature subsets.
public static RandomForestModel trainClassifier(JavaRDD<LabeledPoint> input, int numClasses, java.util.Map<Integer,Integer> categoricalFeaturesInfo, int numTrees, String featureSubsetStrategy, String impurity, int maxDepth, int maxBins, int seed)
RandomForest$.trainClassifier(org.apache.spark.rdd.RDD, org.apache.spark.mllib.tree.configuration.Strategy, int, java.lang.String, int)
input
- (undocumented)numClasses
- (undocumented)categoricalFeaturesInfo
- (undocumented)numTrees
- (undocumented)featureSubsetStrategy
- (undocumented)impurity
- (undocumented)maxDepth
- (undocumented)maxBins
- (undocumented)seed
- (undocumented)
public static RandomForestModel trainRegressor(RDD<LabeledPoint> input, Strategy strategy, int numTrees, String featureSubsetStrategy, int seed)
input
- Training dataset: RDD of LabeledPoint
.
Labels are real numbers.strategy
- Parameters for training each tree in the forest.numTrees
- Number of trees in the random forest.featureSubsetStrategy
- Number of features to consider for splits at each node.
Supported: "auto", "all", "sqrt", "log2", "onethird".
If "auto" is set, this parameter is set based on numTrees:
if numTrees == 1, set to "all";
if numTrees > 1 (forest) set to "onethird".seed
- Random seed for bootstrapping and choosing feature subsets.
public static RandomForestModel trainRegressor(RDD<LabeledPoint> input, scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo, int numTrees, String featureSubsetStrategy, String impurity, int maxDepth, int maxBins, int seed)
input
- Training dataset: RDD of LabeledPoint
.
Labels are real numbers.categoricalFeaturesInfo
- Map storing arity of categorical features.
E.g., an entry (n -> k) indicates that feature n is categorical
with k categories indexed from 0: {0, 1, ..., k-1}.numTrees
- Number of trees in the random forest.featureSubsetStrategy
- Number of features to consider for splits at each node.
Supported: "auto", "all", "sqrt", "log2", "onethird".
If "auto" is set, this parameter is set based on numTrees:
if numTrees == 1, set to "all";
if numTrees > 1 (forest) set to "onethird".impurity
- Criterion used for information gain calculation.
Supported values: "variance".maxDepth
- Maximum depth of the tree.
E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
(suggested value: 4)maxBins
- maximum number of bins used for splitting features
(suggested value: 100)seed
- Random seed for bootstrapping and choosing feature subsets.
public static RandomForestModel trainRegressor(JavaRDD<LabeledPoint> input, java.util.Map<Integer,Integer> categoricalFeaturesInfo, int numTrees, String featureSubsetStrategy, String impurity, int maxDepth, int maxBins, int seed)
RandomForest$.trainRegressor(org.apache.spark.rdd.RDD, org.apache.spark.mllib.tree.configuration.Strategy, int, java.lang.String, int)
input
- (undocumented)categoricalFeaturesInfo
- (undocumented)numTrees
- (undocumented)featureSubsetStrategy
- (undocumented)impurity
- (undocumented)maxDepth
- (undocumented)maxBins
- (undocumented)seed
- (undocumented)
public static String[] supportedFeatureSubsetStrategies()
public RandomForestModel run(RDD<LabeledPoint> input)
input
- Training data: RDD of LabeledPoint
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