org.apache.spark.ml.classification
Class ClassificationModel<FeaturesType,M extends ClassificationModel<FeaturesType,M>>
Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Transformer
org.apache.spark.ml.Model<M>
org.apache.spark.ml.PredictionModel<FeaturesType,M>
org.apache.spark.ml.classification.ClassificationModel<FeaturesType,M>
- All Implemented Interfaces:
- java.io.Serializable, Logging, Params
- Direct Known Subclasses:
- LogisticRegressionModel
public abstract class ClassificationModel<FeaturesType,M extends ClassificationModel<FeaturesType,M>>
- extends PredictionModel<FeaturesType,M>
:: DeveloperApi ::
Model produced by a Classifier
.
Classes are indexed {0, 1, ..., numClasses - 1}.
- See Also:
- Serialized Form
Methods inherited from class Object |
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Methods inherited from interface org.apache.spark.ml.param.Params |
clear, copy, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, setDefault, shouldOwn, validateParams |
Methods inherited from interface org.apache.spark.Logging |
initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning |
ClassificationModel
public ClassificationModel()
setRawPredictionCol
public M setRawPredictionCol(String value)
numClasses
public abstract int numClasses()
- Number of classes (values which the label can take).
transform
public DataFrame transform(DataFrame dataset)
- Transforms dataset by reading from
featuresCol
, and appending new columns as specified by
parameters:
- predicted labels as predictionCol
of type Double
- raw predictions (confidences) as rawPredictionCol
of type Vector
.
- Overrides:
transform
in class PredictionModel<FeaturesType,M extends ClassificationModel<FeaturesType,M>>
- Parameters:
dataset
- input dataset
- Returns:
- transformed dataset
validateAndTransformSchema
public StructType validateAndTransformSchema(StructType schema,
boolean fitting,
DataType featuresDataType)
validateAndTransformSchema
public StructType validateAndTransformSchema(StructType schema,
boolean fitting,
DataType featuresDataType)
- Validates and transforms the input schema with the provided param map.
- Parameters:
schema
- input schemafitting
- whether this is in fittingfeaturesDataType
- SQL DataType for FeaturesType.
E.g., VectorUDT
for vector features.
- Returns:
- output schema