public final class OneVsRestModel extends Model<OneVsRestModel> implements OneVsRestParams, MLWritable
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.
param: labelMetadata Metadata of label column if it exists, or Nominal attribute representing the number of classes in training dataset otherwise. param: models The binary classification models for the reduction. The i-th model is produced by testing the i-th class (taking label 1) vs the rest (taking label 0).
| Modifier and Type | Method and Description |
|---|---|
OneVsRestModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
static OneVsRestModel |
load(String path) |
ClassificationModel[] |
models() |
int |
numClasses() |
int |
numFeatures() |
static MLReader<OneVsRestModel> |
read() |
OneVsRestModel |
setFeaturesCol(String value) |
OneVsRestModel |
setPredictionCol(String value) |
OneVsRestModel |
setRawPredictionCol(String value) |
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms the input dataset.
|
StructType |
transformSchema(StructType schema)
:: DeveloperApi ::
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
transform, transform, transformequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitclassifier, getClassifiervalidateAndTransformSchemagetLabelCol, labelColfeaturesCol, getFeaturesColgetPredictionCol, predictionColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwntoStringgetRawPredictionCol, rawPredictionColgetWeightCol, weightColsaveinitializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarningpublic static MLReader<OneVsRestModel> read()
public static OneVsRestModel load(String path)
public String uid()
Identifiableuid in interface Identifiablepublic ClassificationModel[] models()
public int numClasses()
public int numFeatures()
public OneVsRestModel setFeaturesCol(String value)
public OneVsRestModel setPredictionCol(String value)
public OneVsRestModel setRawPredictionCol(String value)
public StructType transformSchema(StructType schema)
PipelineStageCheck transform validity and derive the output schema from the input schema.
We check validity for interactions between parameters during transformSchema and
raise an exception if any parameter value is invalid. Parameter value checks which
do not depend on other parameters are handled by Param.validate().
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
transformSchema in class PipelineStageschema - (undocumented)public Dataset<Row> transform(Dataset<?> dataset)
Transformertransform in class Transformerdataset - (undocumented)public OneVsRestModel copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Model<OneVsRestModel>extra - (undocumented)public MLWriter write()
MLWritableMLWriter instance for this ML instance.write in interface MLWritable