package classification
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Type Members

trait
ClassificationModel extends Serializable
Represents a classification model that predicts to which of a set of categories an example belongs.
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" )

class
LogisticRegressionModel extends GeneralizedLinearModel with ClassificationModel with Serializable with Saveable with PMMLExportable
Classification model trained using Multinomial/Binary Logistic Regression.
Classification model trained using Multinomial/Binary Logistic Regression.
 Annotations
 @Since( "0.8.0" )

class
LogisticRegressionWithLBFGS extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable
Train a classification model for Multinomial/Binary Logistic Regression using Limitedmemory BFGS.
Train a classification model for Multinomial/Binary Logistic Regression using Limitedmemory BFGS. Standard feature scaling and L2 regularization are used by default.
Earlier implementations of LogisticRegressionWithLBFGS applies a regularization penalty to all elements including the intercept. If this is called with one of standard updaters (L1Updater, or SquaredL2Updater) this is translated into a call to ml.LogisticRegression, otherwise this will use the existing mllib GeneralizedLinearAlgorithm trainer, resulting in a regularization penalty to the intercept.
 Annotations
 @Since( "1.1.0" )
 Note
Labels used in Logistic Regression should be {0, 1, ..., k  1} for k classes multilabel classification problem.

class
LogisticRegressionWithSGD extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable
Train a classification model for Binary Logistic Regression using Stochastic Gradient Descent.
Train a classification model for Binary Logistic Regression using Stochastic Gradient Descent. By default L2 regularization is used, which can be changed via
LogisticRegressionWithSGD.optimizer
.Using LogisticRegressionWithLBFGS is recommended over this.
 Annotations
 @Since( "0.8.0" )
 Note
Labels used in Logistic Regression should be {0, 1, ..., k  1} for k classes multilabel classification problem.

class
NaiveBayes extends Serializable with Logging
Trains a Naive Bayes model given an RDD of
(label, features)
pairs.Trains a Naive Bayes model given an RDD of
(label, features)
pairs.This is the Multinomial NB (see here) which can handle all kinds of discrete data. For example, by converting documents into TFIDF vectors, it can be used for document classification. By making every vector a 01 vector, it can also be used as Bernoulli NB (see here). The input feature values must be nonnegative.
 Annotations
 @Since( "0.9.0" )

class
NaiveBayesModel extends ClassificationModel with Serializable with Saveable
Model for Naive Bayes Classifiers.
Model for Naive Bayes Classifiers.
 Annotations
 @Since( "0.9.0" )

class
SVMModel extends GeneralizedLinearModel with ClassificationModel with Serializable with Saveable with PMMLExportable
Model for Support Vector Machines (SVMs).
Model for Support Vector Machines (SVMs).
 Annotations
 @Since( "0.8.0" )

class
SVMWithSGD extends GeneralizedLinearAlgorithm[SVMModel] with Serializable
Train a Support Vector Machine (SVM) using Stochastic Gradient Descent.
Train a Support Vector Machine (SVM) using Stochastic Gradient Descent. By default L2 regularization is used, which can be changed via
SVMWithSGD.optimizer
. Annotations
 @Since( "0.8.0" )
 Note
Labels used in SVM should be {0, 1}.

class
StreamingLogisticRegressionWithSGD extends StreamingLinearAlgorithm[LogisticRegressionModel, LogisticRegressionWithSGD] with Serializable
Train or predict a logistic regression model on streaming data.
Train or predict a logistic regression model on streaming data. Training uses Stochastic Gradient Descent to update the model based on each new batch of incoming data from a DStream (see
LogisticRegressionWithSGD
for model equation)Each batch of data is assumed to be an RDD of LabeledPoints. The number of data points per batch can vary, but the number of features must be constant. An initial weight vector must be provided.
Use a builder pattern to construct a streaming logistic regression analysis in an application, like:
val model = new StreamingLogisticRegressionWithSGD() .setStepSize(0.5) .setNumIterations(10) .setInitialWeights(Vectors.dense(...)) .trainOn(DStream)
 Annotations
 @Since( "1.3.0" )
Value Members

object
LogisticRegressionModel extends Loader[LogisticRegressionModel] with Serializable
 Annotations
 @Since( "1.3.0" )

object
NaiveBayes extends Serializable
Toplevel methods for calling naive Bayes.
Toplevel methods for calling naive Bayes.
 Annotations
 @Since( "0.9.0" )

object
NaiveBayesModel extends Loader[NaiveBayesModel] with Serializable
 Annotations
 @Since( "1.3.0" )

object
SVMModel extends Loader[SVMModel] with Serializable
 Annotations
 @Since( "1.3.0" )

object
SVMWithSGD extends Serializable
Toplevel methods for calling SVM.
Toplevel methods for calling SVM.
 Annotations
 @Since( "0.8.0" )
 Note
Labels used in SVM should be {0, 1}.