public class NaiveBayesModel extends ProbabilisticClassificationModel<Vector,NaiveBayesModel>
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)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 |
fromOld(NaiveBayesModel oldModel,
NaiveBayes parent)
Convert a model from the old API
|
java.lang.String |
getModelType() |
double |
getSmoothing() |
Param<java.lang.String> |
modelType()
The model type which is a string (case-sensitive).
|
int |
numClasses()
Number of classes (values which the label can take).
|
Vector |
pi() |
protected Vector |
predictRaw(Vector features)
Raw prediction for each possible label.
|
protected Vector |
raw2probabilityInPlace(Vector rawPrediction)
Estimate the probability of each class given the raw prediction,
doing the computation in-place.
|
DoubleParam |
smoothing()
The smoothing parameter.
|
Matrix |
theta() |
java.lang.String |
toString() |
java.lang.String |
uid()
An immutable unique ID for the object and its derivatives.
|
StructType |
validateAndTransformSchema(StructType schema,
boolean fitting,
DataType featuresDataType) |
StructType |
validateAndTransformSchema(StructType schema,
boolean fitting,
DataType featuresDataType)
Validates and transforms the input schema with the provided param map.
|
normalizeToProbabilitiesInPlace, predictProbability, probability2prediction, raw2prediction, raw2probability, setProbabilityCol, setThresholds, transform
predict, setRawPredictionCol
featuresDataType, setFeaturesCol, setPredictionCol, transformImpl, transformSchema
transform, transform, transform
transformSchema
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn, validateParams
initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning
public static NaiveBayesModel fromOld(NaiveBayesModel oldModel, NaiveBayes parent)
public java.lang.String uid()
Identifiable
uid
in interface Identifiable
public Vector pi()
public Matrix theta()
public int numClasses()
ClassificationModel
numClasses
in class ClassificationModel<Vector,NaiveBayesModel>
protected Vector predictRaw(Vector features)
ClassificationModel
transform()
and output rawPredictionCol
.
predictRaw
in class ClassificationModel<Vector,NaiveBayesModel>
features
- (undocumented)protected Vector raw2probabilityInPlace(Vector rawPrediction)
ProbabilisticClassificationModel
This internal method is used to implement transform()
and output probabilityCol
.
raw2probabilityInPlace
in class ProbabilisticClassificationModel<Vector,NaiveBayesModel>
rawPrediction
- (undocumented)public NaiveBayesModel copy(ParamMap extra)
Params
copy
in interface Params
copy
in class Model<NaiveBayesModel>
extra
- (undocumented)defaultCopy()
public java.lang.String toString()
toString
in interface Identifiable
toString
in class java.lang.Object
public DoubleParam smoothing()
public double getSmoothing()
public Param<java.lang.String> modelType()
public java.lang.String getModelType()
public StructType validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
public StructType validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
schema
- input schemafitting
- whether this is in fittingfeaturesDataType
- SQL DataType for FeaturesType.
E.g., VectorUDT
for vector features.