pyspark.ml.classification.
OneVsRestModel
Model fitted by OneVsRest. This stores the models resulting from training k binary classifiers: one for each class. Each example is scored against all k models, and the model with the highest score is picked to label the example.
New in version 2.0.0.
Methods
clear(param)
clear
Clears a param from the param map if it has been explicitly set.
copy([extra])
copy
Creates a copy of this instance with a randomly generated uid and some extra params.
explainParam(param)
explainParam
Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
explainParams()
explainParams
Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap([extra])
extractParamMap
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
getClassifier()
getClassifier
Gets the value of classifier or its default value.
getFeaturesCol()
getFeaturesCol
Gets the value of featuresCol or its default value.
getLabelCol()
getLabelCol
Gets the value of labelCol or its default value.
getOrDefault(param)
getOrDefault
Gets the value of a param in the user-supplied param map or its default value.
getParam(paramName)
getParam
Gets a param by its name.
getPredictionCol()
getPredictionCol
Gets the value of predictionCol or its default value.
getRawPredictionCol()
getRawPredictionCol
Gets the value of rawPredictionCol or its default value.
getWeightCol()
getWeightCol
Gets the value of weightCol or its default value.
hasDefault(param)
hasDefault
Checks whether a param has a default value.
hasParam(paramName)
hasParam
Tests whether this instance contains a param with a given (string) name.
isDefined(param)
isDefined
Checks whether a param is explicitly set by user or has a default value.
isSet(param)
isSet
Checks whether a param is explicitly set by user.
load(path)
load
Reads an ML instance from the input path, a shortcut of read().load(path).
read()
read
Returns an MLReader instance for this class.
save(path)
save
Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
set(param, value)
set
Sets a parameter in the embedded param map.
setFeaturesCol(value)
setFeaturesCol
Sets the value of featuresCol.
featuresCol
setPredictionCol(value)
setPredictionCol
Sets the value of predictionCol.
predictionCol
setRawPredictionCol(value)
setRawPredictionCol
Sets the value of rawPredictionCol.
rawPredictionCol
transform(dataset[, params])
transform
Transforms the input dataset with optional parameters.
write()
write
Returns an MLWriter instance for this ML instance.
Attributes
classifier
labelCol
params
Returns all params ordered by name.
weightCol
Methods Documentation
Creates a copy of this instance with a randomly generated uid and some extra params. This creates a deep copy of the embedded paramMap, and copies the embedded and extra parameters over.
Extra parameters to copy to the new instance
Copy of this instance
extra param values
merged param map
Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
New in version 1.3.0.
pyspark.sql.DataFrame
input dataset
an optional param map that overrides embedded params.
transformed dataset
Attributes Documentation
Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.
dir()
Param