LogisticRegressionModel (Spark 3.5.5 JavaDoc) (original) (raw)

Modifier and Type

Method and Description

[IntParam](../../../../../org/apache/spark/ml/param/IntParam.html "class in org.apache.spark.ml.param")

[aggregationDepth](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#aggregationDepth--)()

Param for suggested depth for treeAggregate (>= 2).

[BinaryLogisticRegressionTrainingSummary](../../../../../org/apache/spark/ml/classification/BinaryLogisticRegressionTrainingSummary.html "interface in org.apache.spark.ml.classification")

[binarySummary](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#binarySummary--)()

Gets summary of model on training set.

[Matrix](../../../../../org/apache/spark/ml/linalg/Matrix.html "interface in org.apache.spark.ml.linalg")

[coefficientMatrix](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#coefficientMatrix--)()

[Vector](../../../../../org/apache/spark/ml/linalg/Vector.html "interface in org.apache.spark.ml.linalg")

[coefficients](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#coefficients--)()

A vector of model coefficients for "binomial" logistic regression.

[LogisticRegressionModel](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html "class in org.apache.spark.ml.classification")

[copy](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#copy-org.apache.spark.ml.param.ParamMap-)([ParamMap](../../../../../org/apache/spark/ml/param/ParamMap.html "class in org.apache.spark.ml.param") extra)

Creates a copy of this instance with the same UID and some extra params.

[DoubleParam](../../../../../org/apache/spark/ml/param/DoubleParam.html "class in org.apache.spark.ml.param")

[elasticNetParam](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#elasticNetParam--)()

Param for the ElasticNet mixing parameter, in range [0, 1].

[LogisticRegressionSummary](../../../../../org/apache/spark/ml/classification/LogisticRegressionSummary.html "interface in org.apache.spark.ml.classification")

[evaluate](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#evaluate-org.apache.spark.sql.Dataset-)([Dataset](../../../../../org/apache/spark/sql/Dataset.html "class in org.apache.spark.sql")<?> dataset)

Evaluates the model on a test dataset.

[Param](../../../../../org/apache/spark/ml/param/Param.html "class in org.apache.spark.ml.param")<String>

[family](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#family--)()

Param for the name of family which is a description of the label distribution to be used in the model.

[BooleanParam](../../../../../org/apache/spark/ml/param/BooleanParam.html "class in org.apache.spark.ml.param")

[fitIntercept](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#fitIntercept--)()

Param for whether to fit an intercept term.

double

[getThreshold](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#getThreshold--)()

Get threshold for binary classification.

double[]

[getThresholds](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#getThresholds--)()

Get thresholds for binary or multiclass classification.

double

[intercept](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#intercept--)()

The model intercept for "binomial" logistic regression.

[Vector](../../../../../org/apache/spark/ml/linalg/Vector.html "interface in org.apache.spark.ml.linalg")

[interceptVector](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#interceptVector--)()

static [LogisticRegressionModel](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html "class in org.apache.spark.ml.classification")

[load](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#load-java.lang.String-)(String path)

[Param](../../../../../org/apache/spark/ml/param/Param.html "class in org.apache.spark.ml.param")<[Matrix](../../../../../org/apache/spark/ml/linalg/Matrix.html "interface in org.apache.spark.ml.linalg")>

[lowerBoundsOnCoefficients](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#lowerBoundsOnCoefficients--)()

The lower bounds on coefficients if fitting under bound constrained optimization.

[Param](../../../../../org/apache/spark/ml/param/Param.html "class in org.apache.spark.ml.param")<[Vector](../../../../../org/apache/spark/ml/linalg/Vector.html "interface in org.apache.spark.ml.linalg")>

[lowerBoundsOnIntercepts](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#lowerBoundsOnIntercepts--)()

The lower bounds on intercepts if fitting under bound constrained optimization.

[DoubleParam](../../../../../org/apache/spark/ml/param/DoubleParam.html "class in org.apache.spark.ml.param")

[maxBlockSizeInMB](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#maxBlockSizeInMB--)()

Param for Maximum memory in MB for stacking input data into blocks.

[IntParam](../../../../../org/apache/spark/ml/param/IntParam.html "class in org.apache.spark.ml.param")

[maxIter](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#maxIter--)()

Param for maximum number of iterations (>= 0).

int

[numClasses](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#numClasses--)()

Number of classes (values which the label can take).

int

[numFeatures](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#numFeatures--)()

Returns the number of features the model was trained on.

double

[predict](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#predict-org.apache.spark.ml.linalg.Vector-)([Vector](../../../../../org/apache/spark/ml/linalg/Vector.html "interface in org.apache.spark.ml.linalg") features)

Predict label for the given feature vector.

[Vector](../../../../../org/apache/spark/ml/linalg/Vector.html "interface in org.apache.spark.ml.linalg")

[predictRaw](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#predictRaw-org.apache.spark.ml.linalg.Vector-)([Vector](../../../../../org/apache/spark/ml/linalg/Vector.html "interface in org.apache.spark.ml.linalg") features)

Raw prediction for each possible label.

static [MLReader](../../../../../org/apache/spark/ml/util/MLReader.html "class in org.apache.spark.ml.util")<[LogisticRegressionModel](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html "class in org.apache.spark.ml.classification")>

[read](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#read--)()

[DoubleParam](../../../../../org/apache/spark/ml/param/DoubleParam.html "class in org.apache.spark.ml.param")

[regParam](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#regParam--)()

Param for regularization parameter (>= 0).

[LogisticRegressionModel](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html "class in org.apache.spark.ml.classification")

[setThreshold](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#setThreshold-double-)(double value)

Set threshold in binary classification, in range [0, 1].

[LogisticRegressionModel](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html "class in org.apache.spark.ml.classification")

[setThresholds](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#setThresholds-double:A-)(double[] value)

Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class.

[BooleanParam](../../../../../org/apache/spark/ml/param/BooleanParam.html "class in org.apache.spark.ml.param")

[standardization](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#standardization--)()

Param for whether to standardize the training features before fitting the model.

[LogisticRegressionTrainingSummary](../../../../../org/apache/spark/ml/classification/LogisticRegressionTrainingSummary.html "interface in org.apache.spark.ml.classification")

[summary](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#summary--)()

Gets summary of model on training set.

[DoubleParam](../../../../../org/apache/spark/ml/param/DoubleParam.html "class in org.apache.spark.ml.param")

[threshold](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#threshold--)()

Param for threshold in binary classification prediction, in range [0, 1].

[DoubleParam](../../../../../org/apache/spark/ml/param/DoubleParam.html "class in org.apache.spark.ml.param")

[tol](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#tol--)()

Param for the convergence tolerance for iterative algorithms (>= 0).

String

[toString](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#toString--)()

String

[uid](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#uid--)()

An immutable unique ID for the object and its derivatives.

[upperBoundsOnCoefficients](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#upperBoundsOnCoefficients--)()

The upper bounds on coefficients if fitting under bound constrained optimization.

[upperBoundsOnIntercepts](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#upperBoundsOnIntercepts--)()

The upper bounds on intercepts if fitting under bound constrained optimization.

[Param](../../../../../org/apache/spark/ml/param/Param.html "class in org.apache.spark.ml.param")<String>

[weightCol](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#weightCol--)()

Param for weight column name.

[MLWriter](../../../../../org/apache/spark/ml/util/MLWriter.html "class in org.apache.spark.ml.util")

[write](../../../../../org/apache/spark/ml/classification/LogisticRegressionModel.html#write--)()

Returns a MLWriter instance for this ML instance.