public class DecisionTree
extends Object
implements scala.Serializable, org.apache.spark.internal.Logging
param: strategy The configuration parameters for the tree algorithm which specify the type of decision tree (classification or regression), feature type (continuous, categorical), depth of the tree, quantile calculation strategy, etc. param: seed Random seed.
Constructor and Description |
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DecisionTree(Strategy strategy) |
Modifier and Type | Method and Description |
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static void |
org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1) |
static org.slf4j.Logger |
org$apache$spark$internal$Logging$$log_() |
DecisionTreeModel |
run(RDD<LabeledPoint> input)
Method to train a decision tree model over an RDD
|
static DecisionTreeModel |
train(RDD<LabeledPoint> input,
scala.Enumeration.Value algo,
Impurity impurity,
int maxDepth)
Method to train a decision tree model.
|
static DecisionTreeModel |
train(RDD<LabeledPoint> input,
scala.Enumeration.Value algo,
Impurity impurity,
int maxDepth,
int numClasses)
Method to train a decision tree model.
|
static DecisionTreeModel |
train(RDD<LabeledPoint> input,
scala.Enumeration.Value algo,
Impurity impurity,
int maxDepth,
int numClasses,
int maxBins,
scala.Enumeration.Value quantileCalculationStrategy,
scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo)
Method to train a decision tree model.
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static DecisionTreeModel |
train(RDD<LabeledPoint> input,
Strategy strategy)
Method to train a decision tree model.
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static DecisionTreeModel |
trainClassifier(JavaRDD<LabeledPoint> input,
int numClasses,
java.util.Map<Integer,Integer> categoricalFeaturesInfo,
String impurity,
int maxDepth,
int maxBins)
Java-friendly API for
org.apache.spark.mllib.tree.DecisionTree.trainClassifier |
static DecisionTreeModel |
trainClassifier(RDD<LabeledPoint> input,
int numClasses,
scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo,
String impurity,
int maxDepth,
int maxBins)
Method to train a decision tree model for binary or multiclass classification.
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static DecisionTreeModel |
trainRegressor(JavaRDD<LabeledPoint> input,
java.util.Map<Integer,Integer> categoricalFeaturesInfo,
String impurity,
int maxDepth,
int maxBins)
Java-friendly API for
org.apache.spark.mllib.tree.DecisionTree.trainRegressor |
static DecisionTreeModel |
trainRegressor(RDD<LabeledPoint> input,
scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo,
String impurity,
int maxDepth,
int maxBins)
Method to train a decision tree model for regression.
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize
public DecisionTree(Strategy strategy)
strategy
- The configuration parameters for the tree algorithm which specify the type
of decision tree (classification or regression), feature type (continuous,
categorical), depth of the tree, quantile calculation strategy, etc.public static DecisionTreeModel train(RDD<LabeledPoint> input, Strategy strategy)
input
- Training dataset: RDD of LabeledPoint
.
For classification, labels should take values {0, 1, ..., numClasses-1}.
For regression, labels are real numbers.strategy
- The configuration parameters for the tree algorithm which specify the type
of decision tree (classification or regression), feature type (continuous,
categorical), depth of the tree, quantile calculation strategy, etc.org.apache.spark.mllib.tree.DecisionTree.trainClassifier
and org.apache.spark.mllib.tree.DecisionTree.trainRegressor
is recommended to clearly separate classification and regression.public static DecisionTreeModel train(RDD<LabeledPoint> input, scala.Enumeration.Value algo, Impurity impurity, int maxDepth)
input
- Training dataset: RDD of LabeledPoint
.
For classification, labels should take values {0, 1, ..., numClasses-1}.
For regression, labels are real numbers.algo
- Type of decision tree, either classification or regression.impurity
- Criterion used for information gain calculation.maxDepth
- Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means
1 internal node + 2 leaf nodes).org.apache.spark.mllib.tree.DecisionTree.trainClassifier
and org.apache.spark.mllib.tree.DecisionTree.trainRegressor
is recommended to clearly separate classification and regression.public static DecisionTreeModel train(RDD<LabeledPoint> input, scala.Enumeration.Value algo, Impurity impurity, int maxDepth, int numClasses)
input
- Training dataset: RDD of LabeledPoint
.
For classification, labels should take values {0, 1, ..., numClasses-1}.
For regression, labels are real numbers.algo
- Type of decision tree, either classification or regression.impurity
- Criterion used for information gain calculation.maxDepth
- Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means
1 internal node + 2 leaf nodes).numClasses
- Number of classes for classification. Default value of 2.org.apache.spark.mllib.tree.DecisionTree.trainClassifier
and org.apache.spark.mllib.tree.DecisionTree.trainRegressor
is recommended to clearly separate classification and regression.public static DecisionTreeModel train(RDD<LabeledPoint> input, scala.Enumeration.Value algo, Impurity impurity, int maxDepth, int numClasses, int maxBins, scala.Enumeration.Value quantileCalculationStrategy, scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo)
input
- Training dataset: RDD of LabeledPoint
.
For classification, labels should take values {0, 1, ..., numClasses-1}.
For regression, labels are real numbers.algo
- Type of decision tree, either classification or regression.impurity
- Criterion used for information gain calculation.maxDepth
- Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means
1 internal node + 2 leaf nodes).numClasses
- Number of classes for classification. Default value of 2.maxBins
- Maximum number of bins used for splitting features.quantileCalculationStrategy
- Algorithm for calculating quantiles.categoricalFeaturesInfo
- Map storing arity of categorical features. An entry (n to k)
indicates that feature n is categorical with k categories
indexed from 0: {0, 1, ..., k-1}.org.apache.spark.mllib.tree.DecisionTree.trainClassifier
and org.apache.spark.mllib.tree.DecisionTree.trainRegressor
is recommended to clearly separate classification and regression.public static DecisionTreeModel trainClassifier(RDD<LabeledPoint> input, int numClasses, scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo, String impurity, int maxDepth, int maxBins)
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. An entry (n to k)
indicates that feature n is categorical with k categories
indexed from 0: {0, 1, ..., k-1}.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: 5)maxBins
- Maximum number of bins used for splitting features.
(suggested value: 32)public static DecisionTreeModel trainClassifier(JavaRDD<LabeledPoint> input, int numClasses, java.util.Map<Integer,Integer> categoricalFeaturesInfo, String impurity, int maxDepth, int maxBins)
org.apache.spark.mllib.tree.DecisionTree.trainClassifier
input
- (undocumented)numClasses
- (undocumented)categoricalFeaturesInfo
- (undocumented)impurity
- (undocumented)maxDepth
- (undocumented)maxBins
- (undocumented)public static DecisionTreeModel trainRegressor(RDD<LabeledPoint> input, scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo, String impurity, int maxDepth, int maxBins)
input
- Training dataset: RDD of LabeledPoint
.
Labels are real numbers.categoricalFeaturesInfo
- Map storing arity of categorical features. An entry (n to k)
indicates that feature n is categorical with k categories
indexed from 0: {0, 1, ..., k-1}.impurity
- Criterion used for information gain calculation.
The only supported value for regression is "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: 5)maxBins
- Maximum number of bins used for splitting features.
(suggested value: 32)public static DecisionTreeModel trainRegressor(JavaRDD<LabeledPoint> input, java.util.Map<Integer,Integer> categoricalFeaturesInfo, String impurity, int maxDepth, int maxBins)
org.apache.spark.mllib.tree.DecisionTree.trainRegressor
input
- (undocumented)categoricalFeaturesInfo
- (undocumented)impurity
- (undocumented)maxDepth
- (undocumented)maxBins
- (undocumented)public static org.slf4j.Logger org$apache$spark$internal$Logging$$log_()
public static void org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)
public DecisionTreeModel run(RDD<LabeledPoint> input)
input
- Training data: RDD of LabeledPoint
.