public class NaiveBayesModel extends ProbabilisticClassificationModel<Vector,NaiveBayesModel> implements 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)Modifier and Type | Method and Description |
---|---|
NaiveBayesModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
Param<String> |
featuresCol()
Param for features column name.
|
String |
getFeaturesCol() |
String |
getLabelCol() |
String |
getModelType() |
String |
getPredictionCol() |
String |
getRawPredictionCol() |
double |
getSmoothing() |
Param<String> |
labelCol()
Param for label column name.
|
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() |
Param<String> |
predictionCol()
Param for prediction column name.
|
Param<String> |
rawPredictionCol()
Param for raw prediction (a.k.a.
|
static MLReader<NaiveBayesModel> |
read() |
DoubleParam |
smoothing()
The smoothing parameter.
|
Matrix |
theta() |
String |
toString() |
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.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
normalizeToProbabilitiesInPlace, setProbabilityCol, setThresholds, transform
setRawPredictionCol
setFeaturesCol, setPredictionCol, transformSchema
transform, transform, transform
save
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 MLReader<NaiveBayesModel> read()
public static NaiveBayesModel load(String path)
public String uid()
Identifiable
uid
in interface Identifiable
public Vector pi()
public Matrix theta()
public int numFeatures()
PredictionModel
numFeatures
in class PredictionModel<Vector,NaiveBayesModel>
public int numClasses()
ClassificationModel
numClasses
in class ClassificationModel<Vector,NaiveBayesModel>
public NaiveBayesModel copy(ParamMap extra)
Params
copy
in interface Params
copy
in class Model<NaiveBayesModel>
extra
- (undocumented)defaultCopy()
public String toString()
toString
in interface Identifiable
toString
in class Object
public MLWriter write()
MLWritable
MLWriter
instance for this ML instance.write
in interface MLWritable
public DoubleParam smoothing()
public double getSmoothing()
public Param<String> modelType()
public String getModelType()
public StructType validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
public Param<String> rawPredictionCol()
public String getRawPredictionCol()
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.public Param<String> labelCol()
public String getLabelCol()
public Param<String> featuresCol()
public String getFeaturesCol()
public Param<String> predictionCol()
public String getPredictionCol()