public interface LogisticRegressionParams extends ProbabilisticClassifierParams, HasRegParam, HasElasticNetParam, HasMaxIter, HasFitIntercept, HasTol, HasStandardization, HasWeightCol, HasThreshold, HasAggregationDepth
| Modifier and Type | Method and Description |
|---|---|
void |
checkThresholdConsistency()
If
threshold and thresholds are both set, ensures they are consistent. |
Param<String> |
family()
Param for the name of family which is a description of the label distribution
to be used in the model.
|
String |
getFamily() |
Matrix |
getLowerBoundsOnCoefficients() |
Vector |
getLowerBoundsOnIntercepts() |
double |
getThreshold()
Get threshold for binary classification.
|
double[] |
getThresholds()
Get thresholds for binary or multiclass classification.
|
Matrix |
getUpperBoundsOnCoefficients() |
Vector |
getUpperBoundsOnIntercepts() |
Param<Matrix> |
lowerBoundsOnCoefficients()
The lower bounds on coefficients if fitting under bound constrained optimization.
|
Param<Vector> |
lowerBoundsOnIntercepts()
The lower bounds on intercepts if fitting under bound constrained optimization.
|
LogisticRegressionParams |
setThreshold(double value)
Set threshold in binary classification, in range [0, 1].
|
LogisticRegressionParams |
setThresholds(double[] value)
Set thresholds in multiclass (or binary) classification to adjust the probability of
predicting each class.
|
Param<Matrix> |
upperBoundsOnCoefficients()
The upper bounds on coefficients if fitting under bound constrained optimization.
|
Param<Vector> |
upperBoundsOnIntercepts()
The upper bounds on intercepts if fitting under bound constrained optimization.
|
boolean |
usingBoundConstrainedOptimization() |
StructType |
validateAndTransformSchema(StructType schema,
boolean fitting,
DataType featuresDataType)
Validates and transforms the input schema with the provided param map.
|
getLabelCol, labelColfeaturesCol, getFeaturesColgetPredictionCol, predictionColclear, copy, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwntoString, uidgetRawPredictionCol, rawPredictionColgetProbabilityCol, probabilityColthresholdsgetRegParam, regParamelasticNetParam, getElasticNetParamgetMaxIter, maxIterfitIntercept, getFitInterceptgetStandardization, standardizationgetWeightCol, weightColthresholdaggregationDepth, getAggregationDepthvoid checkThresholdConsistency()
threshold and thresholds are both set, ensures they are consistent.
IllegalArgumentException - if threshold and thresholds are not equivalentParam<String> family()
String getFamily()
Matrix getLowerBoundsOnCoefficients()
Vector getLowerBoundsOnIntercepts()
double getThreshold()
If thresholds is set with length 2 (i.e., binary classification),
this returns the equivalent threshold:
1 / (1 + thresholds(0) / thresholds(1))getThreshold in interface HasThresholddouble[] getThresholds()
If thresholds is set, return its value.
Otherwise, if threshold is set, return the equivalent thresholds for binary
classification: (1-threshold, threshold).
If neither are set, throw an exception.
getThresholds in interface HasThresholdsMatrix getUpperBoundsOnCoefficients()
Vector getUpperBoundsOnIntercepts()
Param<Matrix> lowerBoundsOnCoefficients()
Param<Vector> lowerBoundsOnIntercepts()
LogisticRegressionParams setThreshold(double value)
If the estimated probability of class label 1 is greater than threshold, then predict 1, else 0. A high threshold encourages the model to predict 0 more often; a low threshold encourages the model to predict 1 more often.
Note: Calling this with threshold p is equivalent to calling setThresholds(Array(1-p, p)).
When setThreshold() is called, any user-set value for thresholds will be cleared.
If both threshold and thresholds are set in a ParamMap, then they must be
equivalent.
Default is 0.5.
value - (undocumented)LogisticRegressionParams setThresholds(double[] value)
Note: When setThresholds() is called, any user-set value for threshold will be cleared.
If both threshold and thresholds are set in a ParamMap, then they must be
equivalent.
value - (undocumented)Param<Matrix> upperBoundsOnCoefficients()
Param<Vector> upperBoundsOnIntercepts()
boolean usingBoundConstrainedOptimization()
StructType validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
PredictorParamsvalidateAndTransformSchema in interface ClassifierParamsvalidateAndTransformSchema in interface PredictorParamsvalidateAndTransformSchema in interface ProbabilisticClassifierParamsschema - input schemafitting - whether this is in fittingfeaturesDataType - SQL DataType for FeaturesType.
E.g., VectorUDT for vector features.