public class NaiveBayes
extends Object
implements scala.Serializable, org.apache.spark.internal.Logging
(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.
| Constructor and Description |
|---|
NaiveBayes() |
NaiveBayes(double lambda) |
| Modifier and Type | Method and Description |
|---|---|
double |
getLambda()
Get the smoothing parameter.
|
String |
getModelType()
Get the model type.
|
NaiveBayesModel |
run(RDD<LabeledPoint> data)
Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.
|
NaiveBayes |
setLambda(double lambda)
Set the smoothing parameter.
|
NaiveBayes |
setModelType(String modelType)
Set the model type using a string (case-sensitive).
|
static NaiveBayesModel |
train(RDD<LabeledPoint> input)
Trains a Naive Bayes model given an RDD of
(label, features) pairs. |
static NaiveBayesModel |
train(RDD<LabeledPoint> input,
double lambda)
Trains a Naive Bayes model given an RDD of
(label, features) pairs. |
static NaiveBayesModel |
train(RDD<LabeledPoint> input,
double lambda,
String modelType)
Trains a Naive Bayes model given an RDD of
(label, features) pairs. |
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitializepublic static NaiveBayesModel train(RDD<LabeledPoint> input)
(label, features) pairs.
This is the default 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.
This version of the method uses a default smoothing parameter of 1.0.
input - RDD of (label, array of features) pairs. Every vector should be a frequency
vector or a count vector.public static NaiveBayesModel train(RDD<LabeledPoint> input, double lambda)
(label, features) pairs.
This is the default 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.
input - RDD of (label, array of features) pairs. Every vector should be a frequency
vector or a count vector.lambda - The smoothing parameterpublic static NaiveBayesModel train(RDD<LabeledPoint> input, double lambda, String modelType)
(label, features) pairs.
The model type can be set to either Multinomial NB (see here) or Bernoulli NB (see here). The Multinomial NB can handle discrete count data and can be called by setting the model type to "multinomial". For example, it can be used with word counts or TF_IDF vectors of documents. The Bernoulli model fits presence or absence (0-1) counts. By making every vector a 0-1 vector and setting the model type to "bernoulli", the fits and predicts as Bernoulli NB.
input - RDD of (label, array of features) pairs. Every vector should be a frequency
vector or a count vector.lambda - The smoothing parameter
modelType - The type of NB model to fit from the enumeration NaiveBayesModels, can be
multinomial or bernoullipublic NaiveBayes setLambda(double lambda)
public double getLambda()
public NaiveBayes setModelType(String modelType)
modelType - (undocumented)public String getModelType()
public NaiveBayesModel run(RDD<LabeledPoint> data)
data - RDD of LabeledPoint.