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package root

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root
package org

Definition Classes
root
package apache

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org
package spark
Core Spark functionality.
Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.
In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.
Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.
Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.
Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

Definition Classes
apache
package mllib
RDD-based machine learning APIs (in maintenance mode).
RDD-based machine learning APIs (in maintenance mode).
The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,
- no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
- bug fixes in the RDD-based APIs will still be accepted.
The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.
Definition Classes
spark
See also
SPARK-4591 to track the progress of feature parity
package classification

Definition Classes
mllib
ClassificationModel
LogisticRegressionModel
LogisticRegressionWithLBFGS
LogisticRegressionWithSGD
NaiveBayes
NaiveBayesModel
SVMModel
SVMWithSGD
StreamingLogisticRegressionWithSGD

org.apache.spark.mllib.classification

ClassificationModel

trait ClassificationModel extends Serializable

Represents a classification model that predicts to which of a set of categories an example belongs. The categories are represented by double values: 0.0, 1.0, 2.0, etc.

Annotations: @Since( "0.8.0" )
Source: ClassificationModel.scala

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Serializable, Serializable, AnyRef, Any

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LogisticRegressionModel, NaiveBayesModel, SVMModel

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ClassificationModel
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abstract def predict(testData: Vector): Double
Predict values for a single data point using the model trained.
Predict values for a single data point using the model trained.
testData
array representing a single data point
returns
predicted category from the trained model

Annotations
@Since( "1.0.0" )
abstract def predict(testData: RDD[Vector]): RDD[Double]
Predict values for the given data set using the model trained.
Predict values for the given data set using the model trained.
testData
RDD representing data points to be predicted
returns
an RDD[Double] where each entry contains the corresponding prediction

Annotations
@Since( "1.0.0" )

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def predict(testData: JavaRDD[Vector]): JavaRDD[Double]
Predict values for examples stored in a JavaRDD.
Predict values for examples stored in a JavaRDD.
testData
JavaRDD representing data points to be predicted
returns
a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction

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@Since( "1.0.0" )
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Packages

ClassificationModel

trait ClassificationModel extends Serializable

Abstract Value Members

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Inherited from Serializable

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ClassificationModel 

trait ClassificationModel extends Serializable

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ClassificationModel