public class NaiveBayesModel extends ProbabilisticClassificationModel<Vector,NaiveBayesModel> implements NaiveBayesParams, MLWritable
NaiveBayes
param: pi log of class priors, whose dimension is C (number of classes) param: theta log of class conditional probabilities, whose dimension is C (number of classes) by D (number of features) param: sigma variance of each feature, whose dimension is C (number of classes) by D (number of features). This matrix is only available when modelType is set Gaussian.
| Modifier and Type | Method and Description |
|---|---|
NaiveBayesModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
static NaiveBayesModel |
load(String path) |
Param<String> |
modelType()
The model type which is a string (case-sensitive).
|
int |
numClasses()
Number of classes (values which the label can take).
|
int |
numFeatures()
Returns the number of features the model was trained on.
|
Vector |
pi() |
Vector |
predictRaw(Vector features)
Raw prediction for each possible label.
|
static MLReader<NaiveBayesModel> |
read() |
Matrix |
sigma() |
DoubleParam |
smoothing()
The smoothing parameter.
|
Matrix |
theta() |
String |
toString() |
String |
uid()
An immutable unique ID for the object and its derivatives.
|
Param<String> |
weightCol()
Param for weight column name.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
normalizeToProbabilitiesInPlace, predictProbability, probabilityCol, setProbabilityCol, setThresholds, thresholds, transform, transformSchemapredict, rawPredictionCol, setRawPredictionCol, transformImplfeaturesCol, labelCol, predictionCol, setFeaturesCol, setPredictionColtransform, transform, transformparamsgetModelType, getSmoothingvalidateAndTransformSchemagetLabelCol, labelColfeaturesCol, getFeaturesColgetPredictionCol, predictionColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwngetWeightColsavevalidateAndTransformSchemagetRawPredictionCol, rawPredictionColgetProbabilityColgetThresholds$init$, 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 MLReader<NaiveBayesModel> read()
public static NaiveBayesModel load(String path)
public final DoubleParam smoothing()
NaiveBayesParamssmoothing in interface NaiveBayesParamspublic final Param<String> modelType()
NaiveBayesParamsmodelType in interface NaiveBayesParamspublic final Param<String> weightCol()
HasWeightColweightCol in interface HasWeightColpublic String uid()
Identifiableuid in interface Identifiablepublic Vector pi()
public Matrix theta()
public Matrix sigma()
public int numFeatures()
PredictionModelnumFeatures in class PredictionModel<Vector,NaiveBayesModel>public int numClasses()
ClassificationModelnumClasses in class ClassificationModel<Vector,NaiveBayesModel>public Vector predictRaw(Vector features)
ClassificationModeltransform() and output rawPredictionCol.
predictRaw in class ClassificationModel<Vector,NaiveBayesModel>features - (undocumented)public NaiveBayesModel copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Model<NaiveBayesModel>extra - (undocumented)public String toString()
toString in interface IdentifiabletoString in class Objectpublic MLWriter write()
MLWritableMLWriter instance for this ML instance.write in interface MLWritable