You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@spark.apache.org by me...@apache.org on 2016/02/26 17:30:35 UTC
spark git commit: [SPARK-12634][PYSPARK][DOC] PySpark tree parameter
desc to consistent format
Repository: spark
Updated Branches:
refs/heads/master 99dfcedbf -> b33261f91
[SPARK-12634][PYSPARK][DOC] PySpark tree parameter desc to consistent format
Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the tree module.
closes #10601
Author: Bryan Cutler <cu...@gmail.com>
Author: vijaykiran <ma...@vijaykiran.com>
Closes #11353 from BryanCutler/param-desc-consistent-tree-SPARK-12634.
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/b33261f9
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/b33261f9
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/b33261f9
Branch: refs/heads/master
Commit: b33261f91387904c5aaccae40f86922c92a4e09a
Parents: 99dfced
Author: Bryan Cutler <cu...@gmail.com>
Authored: Fri Feb 26 08:30:32 2016 -0800
Committer: Xiangrui Meng <me...@databricks.com>
Committed: Fri Feb 26 08:30:32 2016 -0800
----------------------------------------------------------------------
docs/mllib-decision-tree.md | 6 +-
.../apache/spark/mllib/tree/DecisionTree.scala | 172 +++++-----
.../spark/mllib/tree/GradientBoostedTrees.scala | 18 +-
.../apache/spark/mllib/tree/RandomForest.scala | 69 ++--
.../mllib/tree/configuration/Strategy.scala | 13 +-
python/pyspark/mllib/tree.py | 339 +++++++++++--------
6 files changed, 340 insertions(+), 277 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/spark/blob/b33261f9/docs/mllib-decision-tree.md
----------------------------------------------------------------------
diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md
index a8612b6..9af4835 100644
--- a/docs/mllib-decision-tree.md
+++ b/docs/mllib-decision-tree.md
@@ -121,12 +121,12 @@ The parameters are listed below roughly in order of descending importance. New
These parameters describe the problem you want to solve and your dataset.
They should be specified and do not require tuning.
-* **`algo`**: `Classification` or `Regression`
+* **`algo`**: Type of decision tree, either `Classification` or `Regression`.
-* **`numClasses`**: Number of classes (for `Classification` only)
+* **`numClasses`**: Number of classes (for `Classification` only).
* **`categoricalFeaturesInfo`**: Specifies which features are categorical and how many categorical values each of those features can take. This is given as a map from feature indices to feature arity (number of categories). Any features not in this map are treated as continuous.
- * E.g., `Map(0 -> 2, 4 -> 10)` specifies that feature `0` is binary (taking values `0` or `1`) and that feature `4` has 10 categories (values `{0, 1, ..., 9}`). Note that feature indices are 0-based: features `0` and `4` are the 1st and 5th elements of an instance's feature vector.
+ * For example, `Map(0 -> 2, 4 -> 10)` specifies that feature `0` is binary (taking values `0` or `1`) and that feature `4` has 10 categories (values `{0, 1, ..., 9}`). Note that feature indices are 0-based: features `0` and `4` are the 1st and 5th elements of an instance's feature vector.
* Note that you do not have to specify `categoricalFeaturesInfo`. The algorithm will still run and may get reasonable results. However, performance should be better if categorical features are properly designated.
### Stopping criteria
http://git-wip-us.apache.org/repos/asf/spark/blob/b33261f9/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index 51235a2..40440d5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -38,8 +38,9 @@ import org.apache.spark.util.random.XORShiftRandom
/**
* A class which implements a decision tree learning algorithm for classification and regression.
* It supports both continuous and categorical features.
+ *
* @param strategy The configuration parameters for the tree algorithm which specify the type
- * of algorithm (classification, regression, etc.), feature type (continuous,
+ * of decision tree (classification or regression), feature type (continuous,
* categorical), depth of the tree, quantile calculation strategy, etc.
*/
@Since("1.0.0")
@@ -50,8 +51,8 @@ class DecisionTree @Since("1.0.0") (private val strategy: Strategy)
/**
* Method to train a decision tree model over an RDD
- * @param input Training data: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]
- * @return DecisionTreeModel that can be used for prediction
+ * @param input Training data: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
+ * @return DecisionTreeModel that can be used for prediction.
*/
@Since("1.2.0")
def run(input: RDD[LabeledPoint]): DecisionTreeModel = {
@@ -77,9 +78,9 @@ object DecisionTree extends Serializable with Logging {
* For classification, labels should take values {0, 1, ..., numClasses-1}.
* For regression, labels are real numbers.
* @param strategy The configuration parameters for the tree algorithm which specify the type
- * of algorithm (classification, regression, etc.), feature type (continuous,
+ * of decision tree (classification or regression), feature type (continuous,
* categorical), depth of the tree, quantile calculation strategy, etc.
- * @return DecisionTreeModel that can be used for prediction
+ * @return DecisionTreeModel that can be used for prediction.
*/
@Since("1.0.0")
def train(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeModel = {
@@ -97,11 +98,11 @@ object DecisionTree extends Serializable with Logging {
* @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
* For classification, labels should take values {0, 1, ..., numClasses-1}.
* For regression, labels are real numbers.
- * @param algo algorithm, classification or regression
- * @param impurity impurity criterion used for information gain calculation
- * @param maxDepth Maximum depth of the tree.
- * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
- * @return DecisionTreeModel that can be used for prediction
+ * @param algo Type of decision tree, either classification or regression.
+ * @param impurity Criterion used for information gain calculation.
+ * @param maxDepth Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means
+ * 1 internal node + 2 leaf nodes).
+ * @return DecisionTreeModel that can be used for prediction.
*/
@Since("1.0.0")
def train(
@@ -124,12 +125,12 @@ object DecisionTree extends Serializable with Logging {
* @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
* For classification, labels should take values {0, 1, ..., numClasses-1}.
* For regression, labels are real numbers.
- * @param algo algorithm, classification or regression
- * @param impurity impurity criterion used for information gain calculation
- * @param maxDepth Maximum depth of the tree.
- * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
- * @param numClasses number of classes for classification. Default value of 2.
- * @return DecisionTreeModel that can be used for prediction
+ * @param algo Type of decision tree, either classification or regression.
+ * @param impurity Criterion used for information gain calculation.
+ * @param maxDepth Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means
+ * 1 internal node + 2 leaf nodes).
+ * @param numClasses Number of classes for classification. Default value of 2.
+ * @return DecisionTreeModel that can be used for prediction.
*/
@Since("1.2.0")
def train(
@@ -153,17 +154,17 @@ object DecisionTree extends Serializable with Logging {
* @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
* For classification, labels should take values {0, 1, ..., numClasses-1}.
* For regression, labels are real numbers.
- * @param algo classification or regression
- * @param impurity criterion used for information gain calculation
- * @param maxDepth Maximum depth of the tree.
- * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
- * @param numClasses number of classes for classification. Default value of 2.
- * @param maxBins maximum number of bins used for splitting features
- * @param quantileCalculationStrategy algorithm for calculating quantiles
- * @param 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}.
- * @return DecisionTreeModel that can be used for prediction
+ * @param algo Type of decision tree, either classification or regression.
+ * @param impurity Criterion used for information gain calculation.
+ * @param maxDepth Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means
+ * 1 internal node + 2 leaf nodes).
+ * @param numClasses Number of classes for classification. Default value of 2.
+ * @param maxBins Maximum number of bins used for splitting features.
+ * @param quantileCalculationStrategy Algorithm for calculating quantiles.
+ * @param categoricalFeaturesInfo Map storing arity of categorical features. An entry (n -> k)
+ * indicates that feature n is categorical with k categories
+ * indexed from 0: {0, 1, ..., k-1}.
+ * @return DecisionTreeModel that can be used for prediction.
*/
@Since("1.0.0")
def train(
@@ -185,18 +186,18 @@ object DecisionTree extends Serializable with Logging {
*
* @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
* Labels should take values {0, 1, ..., numClasses-1}.
- * @param numClasses number of classes for classification.
- * @param 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}.
+ * @param numClasses Number of classes for classification.
+ * @param categoricalFeaturesInfo Map storing arity of categorical features. An entry (n -> k)
+ * indicates that feature n is categorical with k categories
+ * indexed from 0: {0, 1, ..., k-1}.
* @param impurity Criterion used for information gain calculation.
* Supported values: "gini" (recommended) or "entropy".
- * @param 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)
- * @param maxBins maximum number of bins used for splitting features
- * (suggested value: 32)
- * @return DecisionTreeModel that can be used for prediction
+ * @param 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)
+ * @param maxBins Maximum number of bins used for splitting features.
+ * (suggested value: 32)
+ * @return DecisionTreeModel that can be used for prediction.
*/
@Since("1.1.0")
def trainClassifier(
@@ -232,17 +233,17 @@ object DecisionTree extends Serializable with Logging {
*
* @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
* Labels are real numbers.
- * @param 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}.
+ * @param categoricalFeaturesInfo Map storing arity of categorical features. An entry (n -> k)
+ * indicates that feature n is categorical with k categories
+ * indexed from 0: {0, 1, ..., k-1}.
* @param impurity Criterion used for information gain calculation.
- * Supported values: "variance".
- * @param 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)
- * @param maxBins maximum number of bins used for splitting features
- * (suggested value: 32)
- * @return DecisionTreeModel that can be used for prediction
+ * The only supported value for regression is "variance".
+ * @param 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)
+ * @param maxBins Maximum number of bins used for splitting features.
+ * (suggested value: 32)
+ * @return DecisionTreeModel that can be used for prediction.
*/
@Since("1.1.0")
def trainRegressor(
@@ -277,7 +278,7 @@ object DecisionTree extends Serializable with Logging {
*
* @param node Node in tree from which to classify the given data point.
* @param binnedFeatures Binned feature vector for data point.
- * @param bins possible bins for all features, indexed (numFeatures)(numBins)
+ * @param bins Possible bins for all features, indexed (numFeatures)(numBins).
* @param unorderedFeatures Set of indices of unordered features.
* @return Leaf index if the data point reaches a leaf.
* Otherwise, last node reachable in tree matching this example.
@@ -333,12 +334,12 @@ object DecisionTree extends Serializable with Logging {
* For unordered features, bins correspond to subsets of categories; either the left or right bin
* for each subset is updated.
*
- * @param agg Array storing aggregate calculation, with a set of sufficient statistics for
- * each (feature, bin).
- * @param treePoint Data point being aggregated.
- * @param splits possible splits indexed (numFeatures)(numSplits)
- * @param unorderedFeatures Set of indices of unordered features.
- * @param instanceWeight Weight (importance) of instance in dataset.
+ * @param agg Array storing aggregate calculation, with a set of sufficient statistics for
+ * each (feature, bin).
+ * @param treePoint Data point being aggregated.
+ * @param splits Possible splits indexed (numFeatures)(numSplits).
+ * @param unorderedFeatures Set of indices of unordered features.
+ * @param instanceWeight Weight (importance) of instance in dataset.
*/
private def mixedBinSeqOp(
agg: DTStatsAggregator,
@@ -394,10 +395,10 @@ object DecisionTree extends Serializable with Logging {
*
* For each feature, the sufficient statistics of one bin are updated.
*
- * @param agg Array storing aggregate calculation, with a set of sufficient statistics for
- * each (feature, bin).
- * @param treePoint Data point being aggregated.
- * @param instanceWeight Weight (importance) of instance in dataset.
+ * @param agg Array storing aggregate calculation, with a set of sufficient statistics for
+ * each (feature, bin).
+ * @param treePoint Data point being aggregated.
+ * @param instanceWeight Weight (importance) of instance in dataset.
*/
private def orderedBinSeqOp(
agg: DTStatsAggregator,
@@ -430,17 +431,17 @@ object DecisionTree extends Serializable with Logging {
/**
* Given a group of nodes, this finds the best split for each node.
*
- * @param input Training data: RDD of [[org.apache.spark.mllib.tree.impl.TreePoint]]
- * @param metadata Learning and dataset metadata
+ * @param input Training data: RDD of [[org.apache.spark.mllib.tree.impl.TreePoint]].
+ * @param metadata Learning and dataset metadata.
* @param topNodes Root node for each tree. Used for matching instances with nodes.
- * @param nodesForGroup Mapping: treeIndex --> nodes to be split in tree
+ * @param nodesForGroup Mapping: treeIndex --> nodes to be split in tree.
* @param treeToNodeToIndexInfo Mapping: treeIndex --> nodeIndex --> nodeIndexInfo,
* where nodeIndexInfo stores the index in the group and the
* feature subsets (if using feature subsets).
- * @param splits possible splits for all features, indexed (numFeatures)(numSplits)
- * @param bins possible bins for all features, indexed (numFeatures)(numBins)
- * @param nodeQueue Queue of nodes to split, with values (treeIndex, node).
- * Updated with new non-leaf nodes which are created.
+ * @param splits Possible splits for all features, indexed (numFeatures)(numSplits).
+ * @param bins Possible bins for all features, indexed (numFeatures)(numBins).
+ * @param nodeQueue Queue of nodes to split, with values (treeIndex, node).
+ * Updated with new non-leaf nodes which are created.
* @param nodeIdCache Node Id cache containing an RDD of Array[Int] where
* each value in the array is the data point's node Id
* for a corresponding tree. This is used to prevent the need
@@ -527,10 +528,10 @@ object DecisionTree extends Serializable with Logging {
* Each data point contributes to one node. For each feature,
* the aggregate sufficient statistics are updated for the relevant bins.
*
- * @param agg Array storing aggregate calculation, with a set of sufficient statistics for
- * each (node, feature, bin).
- * @param baggedPoint Data point being aggregated.
- * @return agg
+ * @param agg Array storing aggregate calculation, with a set of sufficient statistics for
+ * each (node, feature, bin).
+ * @param baggedPoint Data point being aggregated.
+ * @return Array of decision tree statistics.
*/
def binSeqOp(
agg: Array[DTStatsAggregator],
@@ -563,6 +564,7 @@ object DecisionTree extends Serializable with Logging {
/**
* Get node index in group --> features indices map,
* which is a short cut to find feature indices for a node given node index in group
+ *
* @param treeToNodeToIndexInfo
* @return
*/
@@ -719,9 +721,10 @@ object DecisionTree extends Serializable with Logging {
/**
* Calculate the information gain for a given (feature, split) based upon left/right aggregates.
- * @param leftImpurityCalculator left node aggregates for this (feature, split)
- * @param rightImpurityCalculator right node aggregate for this (feature, split)
- * @return information gain and statistics for split
+ *
+ * @param leftImpurityCalculator Left node aggregates for this (feature, split).
+ * @param rightImpurityCalculator Right node aggregate for this (feature, split).
+ * @return Information gain and statistics for split.
*/
private def calculateGainForSplit(
leftImpurityCalculator: ImpurityCalculator,
@@ -771,9 +774,10 @@ object DecisionTree extends Serializable with Logging {
/**
* Calculate predict value for current node, given stats of any split.
* Note that this function is called only once for each node.
- * @param leftImpurityCalculator left node aggregates for a split
- * @param rightImpurityCalculator right node aggregates for a split
- * @return predict value and impurity for current node
+ *
+ * @param leftImpurityCalculator Left node aggregates for a split.
+ * @param rightImpurityCalculator Right node aggregates for a split.
+ * @return Predict value and impurity for current node.
*/
private def calculatePredictImpurity(
leftImpurityCalculator: ImpurityCalculator,
@@ -788,8 +792,9 @@ object DecisionTree extends Serializable with Logging {
/**
* Find the best split for a node.
+ *
* @param binAggregates Bin statistics.
- * @return tuple for best split: (Split, information gain, prediction at node)
+ * @return Tuple for best split: (Split, information gain, prediction at node).
*/
private[tree] def binsToBestSplit(
binAggregates: DTStatsAggregator,
@@ -955,8 +960,8 @@ object DecisionTree extends Serializable with Logging {
* and for multiclass classification with a high-arity feature,
* there is one bin per category.
*
- * @param input Training data: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]
- * @param metadata Learning and dataset metadata
+ * @param input Training data: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
+ * @param metadata Learning and dataset metadata.
* @return A tuple of (splits, bins).
* Splits is an Array of [[org.apache.spark.mllib.tree.model.Split]]
* of size (numFeatures, numSplits).
@@ -1102,12 +1107,13 @@ object DecisionTree extends Serializable with Logging {
* NOTE: Returned number of splits is set based on `featureSamples` and
* could be different from the specified `numSplits`.
* The `numSplits` attribute in the `DecisionTreeMetadata` class will be set accordingly.
- * @param featureSamples feature values of each sample
- * @param metadata decision tree metadata
+ *
+ * @param featureSamples Feature values of each sample.
+ * @param metadata Decision tree metadata.
* NOTE: `metadata.numbins` will be changed accordingly
- * if there are not enough splits to be found
- * @param featureIndex feature index to find splits
- * @return array of splits
+ * if there are not enough splits to be found.
+ * @param featureIndex Feature index to find splits.
+ * @return Array of splits.
*/
private[tree] def findSplitsForContinuousFeature(
featureSamples: Array[Double],
http://git-wip-us.apache.org/repos/asf/spark/blob/b33261f9/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
index 1b71256..d131f5d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
@@ -54,8 +54,9 @@ class GradientBoostedTrees @Since("1.2.0") (private val boostingStrategy: Boosti
/**
* Method to train a gradient boosting model
+ *
* @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
- * @return a gradient boosted trees model that can be used for prediction
+ * @return GradientBoostedTreesModel that can be used for prediction.
*/
@Since("1.2.0")
def run(input: RDD[LabeledPoint]): GradientBoostedTreesModel = {
@@ -82,13 +83,14 @@ class GradientBoostedTrees @Since("1.2.0") (private val boostingStrategy: Boosti
/**
* Method to validate a gradient boosting model
+ *
* @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
* @param validationInput Validation dataset.
* This dataset should be different from the training dataset,
* but it should follow the same distribution.
* E.g., these two datasets could be created from an original dataset
* by using [[org.apache.spark.rdd.RDD.randomSplit()]]
- * @return a gradient boosted trees model that can be used for prediction
+ * @return GradientBoostedTreesModel that can be used for prediction.
*/
@Since("1.4.0")
def runWithValidation(
@@ -132,7 +134,7 @@ object GradientBoostedTrees extends Logging {
* For classification, labels should take values {0, 1, ..., numClasses-1}.
* For regression, labels are real numbers.
* @param boostingStrategy Configuration options for the boosting algorithm.
- * @return a gradient boosted trees model that can be used for prediction
+ * @return GradientBoostedTreesModel that can be used for prediction.
*/
@Since("1.2.0")
def train(
@@ -153,11 +155,11 @@ object GradientBoostedTrees extends Logging {
/**
* Internal method for performing regression using trees as base learners.
- * @param input training dataset
- * @param validationInput validation dataset, ignored if validate is set to false.
- * @param boostingStrategy boosting parameters
- * @param validate whether or not to use the validation dataset.
- * @return a gradient boosted trees model that can be used for prediction
+ * @param input Training dataset.
+ * @param validationInput Validation dataset, ignored if validate is set to false.
+ * @param boostingStrategy Boosting parameters.
+ * @param validate Whether or not to use the validation dataset.
+ * @return GradientBoostedTreesModel that can be used for prediction.
*/
private def boost(
input: RDD[LabeledPoint],
http://git-wip-us.apache.org/repos/asf/spark/blob/b33261f9/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
index 570a76f..b7714b3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
@@ -53,12 +53,12 @@ import org.apache.spark.util.random.SamplingUtils
* random forests]]
*
* @param strategy The configuration parameters for the random forest algorithm which specify
- * the type of algorithm (classification, regression, etc.), feature type
+ * the type of random forest (classification or regression), 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".
+ * Supported values: "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
@@ -121,8 +121,9 @@ private class RandomForest (
/**
* Method to train a decision tree model over an RDD
- * @param input Training data: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]
- * @return a random forest model that can be used for prediction
+ *
+ * @param input Training data: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
+ * @return RandomForestModel that can be used for prediction.
*/
def run(input: RDD[LabeledPoint]): RandomForestModel = {
@@ -269,12 +270,12 @@ object RandomForest extends Serializable with Logging {
* @param strategy Parameters for training each tree in the forest.
* @param numTrees Number of trees in the random forest.
* @param featureSubsetStrategy Number of features to consider for splits at each node.
- * Supported: "auto", "all", "sqrt", "log2", "onethird".
+ * Supported values: "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".
- * @param seed Random seed for bootstrapping and choosing feature subsets.
- * @return a random forest model that can be used for prediction
+ * @param seed Random seed for bootstrapping and choosing feature subsets.
+ * @return RandomForestModel that can be used for prediction.
*/
@Since("1.2.0")
def trainClassifier(
@@ -294,25 +295,25 @@ object RandomForest extends Serializable with Logging {
*
* @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
* Labels should take values {0, 1, ..., numClasses-1}.
- * @param numClasses number of classes for classification.
- * @param 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}.
+ * @param numClasses Number of classes for classification.
+ * @param categoricalFeaturesInfo Map storing arity of categorical features. An entry (n -> k)
+ * indicates that feature n is categorical with k categories
+ * indexed from 0: {0, 1, ..., k-1}.
* @param numTrees Number of trees in the random forest.
* @param featureSubsetStrategy Number of features to consider for splits at each node.
- * Supported: "auto", "all", "sqrt", "log2", "onethird".
+ * Supported values: "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".
* @param impurity Criterion used for information gain calculation.
* Supported values: "gini" (recommended) or "entropy".
- * @param 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)
- * @param maxBins maximum number of bins used for splitting features
- * (suggested value: 100)
- * @param seed Random seed for bootstrapping and choosing feature subsets.
- * @return a random forest model that can be used for prediction
+ * @param 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)
+ * @param maxBins Maximum number of bins used for splitting features
+ * (suggested value: 100)
+ * @param seed Random seed for bootstrapping and choosing feature subsets.
+ * @return RandomForestModel that can be used for prediction.
*/
@Since("1.2.0")
def trainClassifier(
@@ -358,12 +359,12 @@ object RandomForest extends Serializable with Logging {
* @param strategy Parameters for training each tree in the forest.
* @param numTrees Number of trees in the random forest.
* @param featureSubsetStrategy Number of features to consider for splits at each node.
- * Supported: "auto", "all", "sqrt", "log2", "onethird".
+ * Supported values: "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".
- * @param seed Random seed for bootstrapping and choosing feature subsets.
- * @return a random forest model that can be used for prediction
+ * @param seed Random seed for bootstrapping and choosing feature subsets.
+ * @return RandomForestModel that can be used for prediction.
*/
@Since("1.2.0")
def trainRegressor(
@@ -383,24 +384,24 @@ object RandomForest extends Serializable with Logging {
*
* @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
* Labels are real numbers.
- * @param 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}.
+ * @param categoricalFeaturesInfo Map storing arity of categorical features. An entry (n -> k)
+ * indicates that feature n is categorical with k categories
+ * indexed from 0: {0, 1, ..., k-1}.
* @param numTrees Number of trees in the random forest.
* @param featureSubsetStrategy Number of features to consider for splits at each node.
- * Supported: "auto", "all", "sqrt", "log2", "onethird".
+ * Supported values: "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".
* @param impurity Criterion used for information gain calculation.
- * Supported values: "variance".
- * @param 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)
- * @param maxBins maximum number of bins used for splitting features
- * (suggested value: 100)
- * @param seed Random seed for bootstrapping and choosing feature subsets.
- * @return a random forest model that can be used for prediction
+ * The only supported value for regression is "variance".
+ * @param 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)
+ * @param maxBins Maximum number of bins used for splitting features.
+ * (suggested value: 100)
+ * @param seed Random seed for bootstrapping and choosing feature subsets.
+ * @return RandomForestModel that can be used for prediction.
*/
@Since("1.2.0")
def trainRegressor(
http://git-wip-us.apache.org/repos/asf/spark/blob/b33261f9/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index 6c04403..9e3e501 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -34,8 +34,8 @@ import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Impurity, Variance}
* Supported for Classification: [[org.apache.spark.mllib.tree.impurity.Gini]],
* [[org.apache.spark.mllib.tree.impurity.Entropy]].
* Supported for Regression: [[org.apache.spark.mllib.tree.impurity.Variance]].
- * @param maxDepth Maximum depth of the tree.
- * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
+ * @param maxDepth Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means
+ * 1 internal node + 2 leaf nodes).
* @param numClasses Number of classes for classification.
* (Ignored for regression.)
* Default value is 2 (binary classification).
@@ -45,10 +45,9 @@ import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Impurity, Variance}
* @param quantileCalculationStrategy Algorithm for calculating quantiles. Supported:
* [[org.apache.spark.mllib.tree.configuration.QuantileStrategy.Sort]]
* @param categoricalFeaturesInfo A map storing information about the categorical variables and the
- * number of discrete values they take. For example, an entry (n ->
- * k) implies the feature n is categorical with k categories 0,
- * 1, 2, ... , k-1. It's important to note that features are
- * zero-indexed.
+ * number of discrete values they take. An entry (n -> k)
+ * indicates that feature n is categorical with k categories
+ * indexed from 0: {0, 1, ..., k-1}.
* @param minInstancesPerNode Minimum number of instances each child must have after split.
* Default value is 1. If a split cause left or right child
* to have less than minInstancesPerNode,
@@ -60,7 +59,7 @@ import org.apache.spark.mllib.tree.impurity.{Entropy, Gini, Impurity, Variance}
* 256 MB.
* @param subsamplingRate Fraction of the training data used for learning decision tree.
* @param useNodeIdCache If this is true, instead of passing trees to executors, the algorithm will
- * maintain a separate RDD of node Id cache for each row.
+ * maintain a separate RDD of node Id cache for each row.
* @param checkpointInterval How often to checkpoint when the node Id cache gets updated.
* E.g. 10 means that the cache will get checkpointed every 10 updates. If
* the checkpoint directory is not set in
http://git-wip-us.apache.org/repos/asf/spark/blob/b33261f9/python/pyspark/mllib/tree.py
----------------------------------------------------------------------
diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py
index 0001b60..f7ea466 100644
--- a/python/pyspark/mllib/tree.py
+++ b/python/pyspark/mllib/tree.py
@@ -60,8 +60,7 @@ class TreeEnsembleModel(JavaModelWrapper, JavaSaveable):
@since("1.3.0")
def totalNumNodes(self):
"""
- Get total number of nodes, summed over all trees in the
- ensemble.
+ Get total number of nodes, summed over all trees in the ensemble.
"""
return self.call("totalNumNodes")
@@ -92,8 +91,9 @@ class DecisionTreeModel(JavaModelWrapper, JavaSaveable, JavaLoader):
transformation or action.
Call predict directly on the RDD instead.
- :param x: Data point (feature vector),
- or an RDD of data points (feature vectors).
+ :param x:
+ Data point (feature vector), or an RDD of data points (feature
+ vectors).
"""
if isinstance(x, RDD):
return self.call("predict", x.map(_convert_to_vector))
@@ -108,8 +108,9 @@ class DecisionTreeModel(JavaModelWrapper, JavaSaveable, JavaLoader):
@since("1.1.0")
def depth(self):
- """Get depth of tree.
- E.g.: Depth 0 means 1 leaf node. Depth 1 means 1 internal node and 2 leaf nodes.
+ """
+ Get depth of tree (e.g. depth 0 means 1 leaf node, depth 1
+ means 1 internal node + 2 leaf nodes).
"""
return self._java_model.depth()
@@ -152,24 +153,37 @@ class DecisionTree(object):
impurity="gini", maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0):
"""
- Train a DecisionTreeModel for classification.
-
- :param data: Training data: RDD of LabeledPoint.
- Labels are integers {0,1,...,numClasses}.
- :param numClasses: Number of classes for classification.
- :param categoricalFeaturesInfo: Map from categorical feature index
- to number of categories.
- Any feature not in this map
- is treated as continuous.
- :param impurity: Supported values: "entropy" or "gini"
- :param maxDepth: Max depth of tree.
- E.g., depth 0 means 1 leaf node.
- Depth 1 means 1 internal node + 2 leaf nodes.
- :param maxBins: Number of bins used for finding splits at each node.
- :param minInstancesPerNode: Min number of instances required at child
- nodes to create the parent split
- :param minInfoGain: Min info gain required to create a split
- :return: DecisionTreeModel
+ Train a decision tree model for classification.
+
+ :param data:
+ Training data: RDD of LabeledPoint. Labels should take values
+ {0, 1, ..., numClasses-1}.
+ :param numClasses:
+ Number of classes for classification.
+ :param categoricalFeaturesInfo:
+ Map storing arity of categorical features. An entry (n -> k)
+ indicates that feature n is categorical with k categories
+ indexed from 0: {0, 1, ..., k-1}.
+ :param impurity:
+ Criterion used for information gain calculation.
+ Supported values: "gini" or "entropy".
+ (default: "gini")
+ :param maxDepth:
+ Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1
+ means 1 internal node + 2 leaf nodes).
+ (default: 5)
+ :param maxBins:
+ Number of bins used for finding splits at each node.
+ (default: 32)
+ :param minInstancesPerNode:
+ Minimum number of instances required at child nodes to create
+ the parent split.
+ (default: 1)
+ :param minInfoGain:
+ Minimum info gain required to create a split.
+ (default: 0.0)
+ :return:
+ DecisionTreeModel.
Example usage:
@@ -211,23 +225,34 @@ class DecisionTree(object):
impurity="variance", maxDepth=5, maxBins=32, minInstancesPerNode=1,
minInfoGain=0.0):
"""
- Train a DecisionTreeModel for regression.
-
- :param data: Training data: RDD of LabeledPoint.
- Labels are real numbers.
- :param categoricalFeaturesInfo: Map from categorical feature
- index to number of categories.
- Any feature not in this map is treated as continuous.
- :param impurity: Supported values: "variance"
- :param maxDepth: Max depth of tree.
- E.g., depth 0 means 1 leaf node.
- Depth 1 means 1 internal node + 2 leaf nodes.
- :param maxBins: Number of bins used for finding splits at each
- node.
- :param minInstancesPerNode: Min number of instances required at
- child nodes to create the parent split
- :param minInfoGain: Min info gain required to create a split
- :return: DecisionTreeModel
+ Train a decision tree model for regression.
+
+ :param data:
+ Training data: RDD of LabeledPoint. Labels are real numbers.
+ :param categoricalFeaturesInfo:
+ Map storing arity of categorical features. An entry (n -> k)
+ indicates that feature n is categorical with k categories
+ indexed from 0: {0, 1, ..., k-1}.
+ :param impurity:
+ Criterion used for information gain calculation.
+ The only supported value for regression is "variance".
+ (default: "variance")
+ :param maxDepth:
+ Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1
+ means 1 internal node + 2 leaf nodes).
+ (default: 5)
+ :param maxBins:
+ Number of bins used for finding splits at each node.
+ (default: 32)
+ :param minInstancesPerNode:
+ Minimum number of instances required at child nodes to create
+ the parent split.
+ (default: 1)
+ :param minInfoGain:
+ Minimum info gain required to create a split.
+ (default: 0.0)
+ :return:
+ DecisionTreeModel.
Example usage:
@@ -302,34 +327,44 @@ class RandomForest(object):
featureSubsetStrategy="auto", impurity="gini", maxDepth=4, maxBins=32,
seed=None):
"""
- Method to train a decision tree model for binary or multiclass
+ Train a random forest model for binary or multiclass
classification.
- :param data: Training dataset: RDD of LabeledPoint. Labels
- should take values {0, 1, ..., numClasses-1}.
- :param numClasses: number of classes for classification.
- :param 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}.
- :param numTrees: Number of trees in the random forest.
- :param featureSubsetStrategy: Number of features to consider for
- splits at each node.
- Supported: "auto" (default), "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".
- :param impurity: Criterion used for information gain calculation.
- Supported values: "gini" (recommended) or "entropy".
- :param maxDepth: Maximum depth of the tree.
- E.g., depth 0 means 1 leaf node; depth 1 means
- 1 internal node + 2 leaf nodes. (default: 4)
- :param maxBins: maximum number of bins used for splitting
- features
- (default: 32)
- :param seed: Random seed for bootstrapping and choosing feature
- subsets.
- :return: RandomForestModel that can be used for prediction
+ :param data:
+ Training dataset: RDD of LabeledPoint. Labels should take values
+ {0, 1, ..., numClasses-1}.
+ :param numClasses:
+ Number of classes for classification.
+ :param categoricalFeaturesInfo:
+ Map storing arity of categorical features. An entry (n -> k)
+ indicates that feature n is categorical with k categories
+ indexed from 0: {0, 1, ..., k-1}.
+ :param numTrees:
+ Number of trees in the random forest.
+ :param featureSubsetStrategy:
+ Number of features to consider for splits at each node.
+ Supported values: "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".
+ (default: "auto")
+ :param impurity:
+ Criterion used for information gain calculation.
+ Supported values: "gini" or "entropy".
+ (default: "gini")
+ :param maxDepth:
+ Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1
+ means 1 internal node + 2 leaf nodes).
+ (default: 4)
+ :param maxBins:
+ Maximum number of bins used for splitting features.
+ (default: 32)
+ :param seed:
+ Random seed for bootstrapping and choosing feature subsets.
+ Set as None to generate seed based on system time.
+ (default: None)
+ :return:
+ RandomForestModel that can be used for prediction.
Example usage:
@@ -383,32 +418,40 @@ class RandomForest(object):
def trainRegressor(cls, data, categoricalFeaturesInfo, numTrees, featureSubsetStrategy="auto",
impurity="variance", maxDepth=4, maxBins=32, seed=None):
"""
- Method to train a decision tree model for regression.
-
- :param data: Training dataset: RDD of LabeledPoint. Labels are
- real numbers.
- :param 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}.
- :param numTrees: Number of trees in the random forest.
- :param featureSubsetStrategy: Number of features to consider for
- splits at each node.
- Supported: "auto" (default), "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" for regression.
- :param impurity: Criterion used for information gain
- calculation.
- Supported values: "variance".
- :param maxDepth: Maximum depth of the tree. E.g., depth 0 means
- 1 leaf node; depth 1 means 1 internal node + 2 leaf
- nodes. (default: 4)
- :param maxBins: maximum number of bins used for splitting
- features (default: 32)
- :param seed: Random seed for bootstrapping and choosing feature
- subsets.
- :return: RandomForestModel that can be used for prediction
+ Train a random forest model for regression.
+
+ :param data:
+ Training dataset: RDD of LabeledPoint. Labels are real numbers.
+ :param categoricalFeaturesInfo:
+ Map storing arity of categorical features. An entry (n -> k)
+ indicates that feature n is categorical with k categories
+ indexed from 0: {0, 1, ..., k-1}.
+ :param numTrees:
+ Number of trees in the random forest.
+ :param featureSubsetStrategy:
+ Number of features to consider for splits at each node.
+ Supported values: "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" for regression.
+ (default: "auto")
+ :param impurity:
+ Criterion used for information gain calculation.
+ The only supported value for regression is "variance".
+ (default: "variance")
+ :param maxDepth:
+ Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1
+ means 1 internal node + 2 leaf nodes).
+ (default: 4)
+ :param maxBins:
+ Maximum number of bins used for splitting features.
+ (default: 32)
+ :param seed:
+ Random seed for bootstrapping and choosing feature subsets.
+ Set as None to generate seed based on system time.
+ (default: None)
+ :return:
+ RandomForestModel that can be used for prediction.
Example usage:
@@ -480,31 +523,37 @@ class GradientBoostedTrees(object):
loss="logLoss", numIterations=100, learningRate=0.1, maxDepth=3,
maxBins=32):
"""
- Method to train a gradient-boosted trees model for
- classification.
-
- :param data: Training dataset: RDD of LabeledPoint.
- Labels should take values {0, 1}.
- :param 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}.
- :param loss: Loss function used for minimization during gradient
- boosting. Supported: {"logLoss" (default),
- "leastSquaresError", "leastAbsoluteError"}.
- :param numIterations: Number of iterations of boosting.
- (default: 100)
- :param learningRate: Learning rate for shrinking the
- contribution of each estimator. The learning rate
- should be between in the interval (0, 1].
- (default: 0.1)
- :param maxDepth: Maximum depth of the tree. E.g., depth 0 means
- 1 leaf node; depth 1 means 1 internal node + 2 leaf
- nodes. (default: 3)
- :param maxBins: maximum number of bins used for splitting
- features (default: 32) DecisionTree requires maxBins >= max categories
- :return: GradientBoostedTreesModel that can be used for
- prediction
+ Train a gradient-boosted trees model for classification.
+
+ :param data:
+ Training dataset: RDD of LabeledPoint. Labels should take values
+ {0, 1}.
+ :param categoricalFeaturesInfo:
+ Map storing arity of categorical features. An entry (n -> k)
+ indicates that feature n is categorical with k categories
+ indexed from 0: {0, 1, ..., k-1}.
+ :param loss:
+ Loss function used for minimization during gradient boosting.
+ Supported values: "logLoss", "leastSquaresError",
+ "leastAbsoluteError".
+ (default: "logLoss")
+ :param numIterations:
+ Number of iterations of boosting.
+ (default: 100)
+ :param learningRate:
+ Learning rate for shrinking the contribution of each estimator.
+ The learning rate should be between in the interval (0, 1].
+ (default: 0.1)
+ :param maxDepth:
+ Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1
+ means 1 internal node + 2 leaf nodes).
+ (default: 3)
+ :param maxBins:
+ Maximum number of bins used for splitting features. DecisionTree
+ requires maxBins >= max categories.
+ (default: 32)
+ :return:
+ GradientBoostedTreesModel that can be used for prediction.
Example usage:
@@ -543,30 +592,36 @@ class GradientBoostedTrees(object):
loss="leastSquaresError", numIterations=100, learningRate=0.1, maxDepth=3,
maxBins=32):
"""
- Method to train a gradient-boosted trees model for regression.
-
- :param data: Training dataset: RDD of LabeledPoint. Labels are
- real numbers.
- :param 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}.
- :param loss: Loss function used for minimization during gradient
- boosting. Supported: {"logLoss" (default),
- "leastSquaresError", "leastAbsoluteError"}.
- :param numIterations: Number of iterations of boosting.
- (default: 100)
- :param learningRate: Learning rate for shrinking the
- contribution of each estimator. The learning rate
- should be between in the interval (0, 1].
- (default: 0.1)
- :param maxBins: maximum number of bins used for splitting
- features (default: 32) DecisionTree requires maxBins >= max categories
- :param maxDepth: Maximum depth of the tree. E.g., depth 0 means
- 1 leaf node; depth 1 means 1 internal node + 2 leaf
- nodes. (default: 3)
- :return: GradientBoostedTreesModel that can be used for
- prediction
+ Train a gradient-boosted trees model for regression.
+
+ :param data:
+ Training dataset: RDD of LabeledPoint. Labels are real numbers.
+ :param categoricalFeaturesInfo:
+ Map storing arity of categorical features. An entry (n -> k)
+ indicates that feature n is categorical with k categories
+ indexed from 0: {0, 1, ..., k-1}.
+ :param loss:
+ Loss function used for minimization during gradient boosting.
+ Supported values: "logLoss", "leastSquaresError",
+ "leastAbsoluteError".
+ (default: "leastSquaresError")
+ :param numIterations:
+ Number of iterations of boosting.
+ (default: 100)
+ :param learningRate:
+ Learning rate for shrinking the contribution of each estimator.
+ The learning rate should be between in the interval (0, 1].
+ (default: 0.1)
+ :param maxDepth:
+ Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1
+ means 1 internal node + 2 leaf nodes).
+ (default: 3)
+ :param maxBins:
+ Maximum number of bins used for splitting features. DecisionTree
+ requires maxBins >= max categories.
+ (default: 32)
+ :return:
+ GradientBoostedTreesModel that can be used for prediction.
Example usage:
---------------------------------------------------------------------
To unsubscribe, e-mail: commits-unsubscribe@spark.apache.org
For additional commands, e-mail: commits-help@spark.apache.org