[38/51] [partial] spark-website git commit: Add 2.1.2 docs
http://git-wip-us.apache.org/repos/asf/spark-website/blob/a6155a89/site/docs/2.1.2/api/R/spark.gbt.html
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+http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd;>http://www.w3.org/1999/xhtml;>R: Gradient Boosted Tree
Model for Regression and Classification
+
+
+
+https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css;>
+https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js";>
+https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js";>
+hljs.initHighlightingOnLoad();
+
+
+spark.gbt
{SparkR}R Documentation
+
+Gradient Boosted Tree Model for Regression and Classification
+
+Description
+
+spark.gbt fits a Gradient Boosted Tree Regression model or
Classification model on a
+SparkDataFrame. Users can call summary to get a summary of the
fitted
+Gradient Boosted Tree model, predict to make predictions on new
data, and
+write.ml/read.ml to save/load fitted models.
+For more details, see
+http://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-regression;>
+GBT Regression and
+http://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-classifier;>
+GBT Classification
+
+
+
+Usage
+
+
+spark.gbt(data, formula, ...)
+
+## S4 method for signature 'SparkDataFrame,formula'
+spark.gbt(data, formula,
+ type = c("regression", "classification"), maxDepth = 5, maxBins = 32,
+ maxIter = 20, stepSize = 0.1, lossType = NULL, seed = NULL,
+ subsamplingRate = 1, minInstancesPerNode = 1, minInfoGain = 0,
+ checkpointInterval = 10, maxMemoryInMB = 256, cacheNodeIds = FALSE)
+
+## S4 method for signature 'GBTRegressionModel'
+predict(object, newData)
+
+## S4 method for signature 'GBTClassificationModel'
+predict(object, newData)
+
+## S4 method for signature 'GBTRegressionModel,character'
+write.ml(object, path,
+ overwrite = FALSE)
+
+## S4 method for signature 'GBTClassificationModel,character'
+write.ml(object, path,
+ overwrite = FALSE)
+
+## S4 method for signature 'GBTRegressionModel'
+summary(object)
+
+## S4 method for signature 'GBTClassificationModel'
+summary(object)
+
+## S3 method for class 'summary.GBTRegressionModel'
+print(x, ...)
+
+## S3 method for class 'summary.GBTClassificationModel'
+print(x, ...)
+
+
+
+Arguments
+
+
+data
+
+a SparkDataFrame for training.
+
+formula
+
+a symbolic description of the model to be fitted. Currently only a few
formula
+operators are supported, including '~', ':', '+', and '-'.
+
+...
+
+additional arguments passed to the method.
+
+type
+
+type of model, one of regression or classification,
to fit
+
+maxDepth
+
+Maximum depth of the tree (= 0).
+
+maxBins
+
+Maximum number of bins used for discretizing continuous features and for
choosing
+how to split on features at each node. More bins give higher granularity. Must
be
+= 2 and = number of categories in any categorical feature.
+
+maxIter
+
+Param for maximum number of iterations (= 0).
+
+stepSize
+
+Param for Step size to be used for each iteration of optimization.
+
+lossType
+
+Loss function which GBT tries to minimize.
+For classification, must be logistic. For regression, must be one
of
+squared (L2) and absolute (L1), default is
squared.
+
+seed
+
+integer seed for random number generation.
+
+subsamplingRate
+
+Fraction of the training data used for learning each decision tree, in
+range (0, 1].
+
+minInstancesPerNode
+
+Minimum number of instances each child must have after split. If a
+split causes the left or right child to have fewer than
+minInstancesPerNode, the split will be discarded as invalid. Should be
+= 1.
+
+minInfoGain
+
+Minimum information gain for a split to be considered at a tree node.
+
+checkpointInterval
+
+Param for set checkpoint interval (= 1) or disable checkpoint (-1).
+
+maxMemoryInMB
+
+Maximum memory in MB allocated to histogram aggregation.
+
+cacheNodeIds
+
+If FALSE, the algorithm will pass trees to executors to match instances with
+nodes. If TRUE, the algorithm will cache node IDs for each instance. Caching
+can speed up training of deeper trees. Users can set how often should the
+cache be checkpointed or disable it by setting checkpointInterval.
+
+object
+
+A fitted Gradient Boosted Tree regression model or classification model.
+
+newData
+
+a SparkDataFrame for testing.
+
+path
+
+The directory where the model is saved.
+
+overwrite
+
+Overwrites or not if the output path already exists. Default is FALSE
+which means throw exception if the output path exists.
+
+x
+
+summary object of Gradient Boosted Tree regression model or classification
model
+returned by summary.
+
+
+
+
+Value
+
+spark.gbt returns a fitted Gradient Boosted Tree model.
+
[38/51] [partial] spark-website git commit: Add 2.1.2 docs
http://git-wip-us.apache.org/repos/asf/spark-website/blob/a6d9cbde/site/docs/2.1.2/api/R/spark.gbt.html
--
diff --git a/site/docs/2.1.2/api/R/spark.gbt.html
b/site/docs/2.1.2/api/R/spark.gbt.html
new file mode 100644
index 000..98b2b03
--- /dev/null
+++ b/site/docs/2.1.2/api/R/spark.gbt.html
@@ -0,0 +1,244 @@
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd;>http://www.w3.org/1999/xhtml;>R: Gradient Boosted Tree
Model for Regression and Classification
+
+
+
+https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css;>
+https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js";>
+https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js";>
+hljs.initHighlightingOnLoad();
+
+
+spark.gbt
{SparkR}R Documentation
+
+Gradient Boosted Tree Model for Regression and Classification
+
+Description
+
+spark.gbt fits a Gradient Boosted Tree Regression model or
Classification model on a
+SparkDataFrame. Users can call summary to get a summary of the
fitted
+Gradient Boosted Tree model, predict to make predictions on new
data, and
+write.ml/read.ml to save/load fitted models.
+For more details, see
+http://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-regression;>
+GBT Regression and
+http://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-classifier;>
+GBT Classification
+
+
+
+Usage
+
+
+spark.gbt(data, formula, ...)
+
+## S4 method for signature 'SparkDataFrame,formula'
+spark.gbt(data, formula,
+ type = c("regression", "classification"), maxDepth = 5, maxBins = 32,
+ maxIter = 20, stepSize = 0.1, lossType = NULL, seed = NULL,
+ subsamplingRate = 1, minInstancesPerNode = 1, minInfoGain = 0,
+ checkpointInterval = 10, maxMemoryInMB = 256, cacheNodeIds = FALSE)
+
+## S4 method for signature 'GBTRegressionModel'
+predict(object, newData)
+
+## S4 method for signature 'GBTClassificationModel'
+predict(object, newData)
+
+## S4 method for signature 'GBTRegressionModel,character'
+write.ml(object, path,
+ overwrite = FALSE)
+
+## S4 method for signature 'GBTClassificationModel,character'
+write.ml(object, path,
+ overwrite = FALSE)
+
+## S4 method for signature 'GBTRegressionModel'
+summary(object)
+
+## S4 method for signature 'GBTClassificationModel'
+summary(object)
+
+## S3 method for class 'summary.GBTRegressionModel'
+print(x, ...)
+
+## S3 method for class 'summary.GBTClassificationModel'
+print(x, ...)
+
+
+
+Arguments
+
+
+data
+
+a SparkDataFrame for training.
+
+formula
+
+a symbolic description of the model to be fitted. Currently only a few
formula
+operators are supported, including '~', ':', '+', and '-'.
+
+...
+
+additional arguments passed to the method.
+
+type
+
+type of model, one of regression or classification,
to fit
+
+maxDepth
+
+Maximum depth of the tree (= 0).
+
+maxBins
+
+Maximum number of bins used for discretizing continuous features and for
choosing
+how to split on features at each node. More bins give higher granularity. Must
be
+= 2 and = number of categories in any categorical feature.
+
+maxIter
+
+Param for maximum number of iterations (= 0).
+
+stepSize
+
+Param for Step size to be used for each iteration of optimization.
+
+lossType
+
+Loss function which GBT tries to minimize.
+For classification, must be logistic. For regression, must be one
of
+squared (L2) and absolute (L1), default is
squared.
+
+seed
+
+integer seed for random number generation.
+
+subsamplingRate
+
+Fraction of the training data used for learning each decision tree, in
+range (0, 1].
+
+minInstancesPerNode
+
+Minimum number of instances each child must have after split. If a
+split causes the left or right child to have fewer than
+minInstancesPerNode, the split will be discarded as invalid. Should be
+= 1.
+
+minInfoGain
+
+Minimum information gain for a split to be considered at a tree node.
+
+checkpointInterval
+
+Param for set checkpoint interval (= 1) or disable checkpoint (-1).
+
+maxMemoryInMB
+
+Maximum memory in MB allocated to histogram aggregation.
+
+cacheNodeIds
+
+If FALSE, the algorithm will pass trees to executors to match instances with
+nodes. If TRUE, the algorithm will cache node IDs for each instance. Caching
+can speed up training of deeper trees. Users can set how often should the
+cache be checkpointed or disable it by setting checkpointInterval.
+
+object
+
+A fitted Gradient Boosted Tree regression model or classification model.
+
+newData
+
+a SparkDataFrame for testing.
+
+path
+
+The directory where the model is saved.
+
+overwrite
+
+Overwrites or not if the output path already exists. Default is FALSE
+which means throw exception if the output path exists.
+
+x
+
+summary object of Gradient Boosted Tree regression model or classification
model
+returned by summary.
+
+
+
+
+Value
+
+spark.gbt returns a fitted Gradient Boosted Tree model.
+
[38/51] [partial] spark-website git commit: Add 2.1.2 docs
http://git-wip-us.apache.org/repos/asf/spark-website/blob/0b563c84/site/docs/2.1.2/api/R/spark.gbt.html
--
diff --git a/site/docs/2.1.2/api/R/spark.gbt.html
b/site/docs/2.1.2/api/R/spark.gbt.html
new file mode 100644
index 000..98b2b03
--- /dev/null
+++ b/site/docs/2.1.2/api/R/spark.gbt.html
@@ -0,0 +1,244 @@
+http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd;>http://www.w3.org/1999/xhtml;>R: Gradient Boosted Tree
Model for Regression and Classification
+
+
+
+https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/styles/github.min.css;>
+https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/highlight.min.js";>
+https://cdnjs.cloudflare.com/ajax/libs/highlight.js/8.3/languages/r.min.js";>
+hljs.initHighlightingOnLoad();
+
+
+spark.gbt
{SparkR}R Documentation
+
+Gradient Boosted Tree Model for Regression and Classification
+
+Description
+
+spark.gbt fits a Gradient Boosted Tree Regression model or
Classification model on a
+SparkDataFrame. Users can call summary to get a summary of the
fitted
+Gradient Boosted Tree model, predict to make predictions on new
data, and
+write.ml/read.ml to save/load fitted models.
+For more details, see
+http://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-regression;>
+GBT Regression and
+http://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-classifier;>
+GBT Classification
+
+
+
+Usage
+
+
+spark.gbt(data, formula, ...)
+
+## S4 method for signature 'SparkDataFrame,formula'
+spark.gbt(data, formula,
+ type = c("regression", "classification"), maxDepth = 5, maxBins = 32,
+ maxIter = 20, stepSize = 0.1, lossType = NULL, seed = NULL,
+ subsamplingRate = 1, minInstancesPerNode = 1, minInfoGain = 0,
+ checkpointInterval = 10, maxMemoryInMB = 256, cacheNodeIds = FALSE)
+
+## S4 method for signature 'GBTRegressionModel'
+predict(object, newData)
+
+## S4 method for signature 'GBTClassificationModel'
+predict(object, newData)
+
+## S4 method for signature 'GBTRegressionModel,character'
+write.ml(object, path,
+ overwrite = FALSE)
+
+## S4 method for signature 'GBTClassificationModel,character'
+write.ml(object, path,
+ overwrite = FALSE)
+
+## S4 method for signature 'GBTRegressionModel'
+summary(object)
+
+## S4 method for signature 'GBTClassificationModel'
+summary(object)
+
+## S3 method for class 'summary.GBTRegressionModel'
+print(x, ...)
+
+## S3 method for class 'summary.GBTClassificationModel'
+print(x, ...)
+
+
+
+Arguments
+
+
+data
+
+a SparkDataFrame for training.
+
+formula
+
+a symbolic description of the model to be fitted. Currently only a few
formula
+operators are supported, including '~', ':', '+', and '-'.
+
+...
+
+additional arguments passed to the method.
+
+type
+
+type of model, one of regression or classification,
to fit
+
+maxDepth
+
+Maximum depth of the tree (= 0).
+
+maxBins
+
+Maximum number of bins used for discretizing continuous features and for
choosing
+how to split on features at each node. More bins give higher granularity. Must
be
+= 2 and = number of categories in any categorical feature.
+
+maxIter
+
+Param for maximum number of iterations (= 0).
+
+stepSize
+
+Param for Step size to be used for each iteration of optimization.
+
+lossType
+
+Loss function which GBT tries to minimize.
+For classification, must be logistic. For regression, must be one
of
+squared (L2) and absolute (L1), default is
squared.
+
+seed
+
+integer seed for random number generation.
+
+subsamplingRate
+
+Fraction of the training data used for learning each decision tree, in
+range (0, 1].
+
+minInstancesPerNode
+
+Minimum number of instances each child must have after split. If a
+split causes the left or right child to have fewer than
+minInstancesPerNode, the split will be discarded as invalid. Should be
+= 1.
+
+minInfoGain
+
+Minimum information gain for a split to be considered at a tree node.
+
+checkpointInterval
+
+Param for set checkpoint interval (= 1) or disable checkpoint (-1).
+
+maxMemoryInMB
+
+Maximum memory in MB allocated to histogram aggregation.
+
+cacheNodeIds
+
+If FALSE, the algorithm will pass trees to executors to match instances with
+nodes. If TRUE, the algorithm will cache node IDs for each instance. Caching
+can speed up training of deeper trees. Users can set how often should the
+cache be checkpointed or disable it by setting checkpointInterval.
+
+object
+
+A fitted Gradient Boosted Tree regression model or classification model.
+
+newData
+
+a SparkDataFrame for testing.
+
+path
+
+The directory where the model is saved.
+
+overwrite
+
+Overwrites or not if the output path already exists. Default is FALSE
+which means throw exception if the output path exists.
+
+x
+
+summary object of Gradient Boosted Tree regression model or classification
model
+returned by summary.
+
+
+
+
+Value
+
+spark.gbt returns a fitted Gradient Boosted Tree model.
+
