Type Members

1.  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")

2.  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")

3.  class LogisticRegressionWithLBFGS extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable

   Train a classification model for Multinomial/Binary Logistic Regression using Limited-memory BFGS.

   Train a classification model for Multinomial/Binary Logistic Regression using Limited-memory 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 multi-label classification problem.

4.  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 multi-label classification problem.

5.  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 TF-IDF vectors, it can be used for document classification. By making every vector a 0-1 vector, it can also be used as Bernoulli NB (see here). The input feature values must be nonnegative.

   Annotations

   @Since("0.9.0")

6.  class NaiveBayesModel extends ClassificationModel with Serializable with Saveable

   Model for Naive Bayes Classifiers.

   Model for Naive Bayes Classifiers.

   Annotations

   @Since("0.9.0")

7.  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")

8.  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}.

9.  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")