classification

Type Members

1. trait ClassificationModel extends Serializable

   

   :: Experimental :: Represents a classification model that predicts to which of a set of categories an example belongs.

   :: Experimental :: 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

   @Experimental()

2. class LogisticRegressionModel extends GeneralizedLinearModel with ClassificationModel with Serializable

   

   Classification model trained using Logistic Regression.

3. class LogisticRegressionWithLBFGS extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable

   

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

   Train a classification model for Logistic Regression using Limited-memory BFGS. Standard feature scaling and L2 regularization are used by default. NOTE: Labels used in Logistic Regression should be {0, 1}

4. class LogisticRegressionWithSGD extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable

   

   Train a classification model for Logistic Regression using Stochastic Gradient Descent.

   Train a classification model for Logistic Regression using Stochastic Gradient Descent. By default L2 regularization is used, which can be changed via LogisticRegressionWithSGD.optimizer. NOTE: Labels used in Logistic Regression should be {0, 1}. Using LogisticRegressionWithLBFGS is recommended over this.

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 (http://tinyurl.com/lsdw6p) 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 (http://tinyurl.com/p7c96j6). The input feature values must be nonnegative.

6. class NaiveBayesModel extends ClassificationModel with Serializable

   

   Model for Naive Bayes Classifiers.

7. class SVMModel extends GeneralizedLinearModel with ClassificationModel with Serializable

   

   Model for Support Vector Machines (SVMs).

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. NOTE: Labels used in SVM should be {0, 1}.