org.apache.spark.ml.regression

Class LinearRegressionModel

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.regression.RegressionModel<Vector,LinearRegressionModel>

org.apache.spark.ml.regression.LinearRegressionModel

All Implemented Interfaces:

java.io.Serializable, Logging, Params, HasAggregationDepth, HasElasticNetParam, HasFeaturesCol, HasFitIntercept, HasLabelCol, HasLoss, HasMaxIter, HasPredictionCol, HasRegParam, HasSolver, HasStandardization, HasTol, HasWeightCol, PredictorParams, LinearRegressionParams, GeneralMLWritable, Identifiable, MLWritable

public class LinearRegressionModel
extends RegressionModel<Vector,LinearRegressionModel>
implements LinearRegressionParams, GeneralMLWritable

Model produced by LinearRegression.

See Also:

Serialized Form

Method Summary

Modifier and Type	Method and Description
Vector	coefficients()
LinearRegressionModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
LinearRegressionSummary	evaluate(Dataset<?> dataset) Evaluates the model on a test dataset.
boolean	hasSummary() Indicates whether a training summary exists for this model instance.
double	intercept()
static LinearRegressionModel	load(String path)
int	numFeatures() Returns the number of features the model was trained on.
double	predict(Vector features) Predict label for the given features.
static MLReader<LinearRegressionModel>	read()
double	scale()
LinearRegressionTrainingSummary	summary() Gets summary (e.g.
String	uid() An immutable unique ID for the object and its derivatives.
GeneralMLWriter	write() Returns a GeneralMLWriter instance for this ML instance.

Methods inherited from class org.apache.spark.ml.PredictionModel

setFeaturesCol, setPredictionCol, transform, transformSchema

Methods inherited from class org.apache.spark.ml.Model

hasParent, parent, setParent

Methods inherited from class org.apache.spark.ml.Transformer

transform, transform, transform

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.ml.regression.LinearRegressionParams

epsilon, getEpsilon, loss, solver, validateAndTransformSchema

Methods inherited from interface org.apache.spark.ml.param.shared.HasLabelCol

getLabelCol, labelCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol

featuresCol, getFeaturesCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol

getPredictionCol, predictionCol

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString

Methods inherited from interface org.apache.spark.ml.param.shared.HasRegParam

getRegParam, regParam

Methods inherited from interface org.apache.spark.ml.param.shared.HasElasticNetParam

elasticNetParam, getElasticNetParam

Methods inherited from interface org.apache.spark.ml.param.shared.HasMaxIter

getMaxIter, maxIter

Methods inherited from interface org.apache.spark.ml.param.shared.HasTol

getTol, tol

Methods inherited from interface org.apache.spark.ml.param.shared.HasFitIntercept

fitIntercept, getFitIntercept

Methods inherited from interface org.apache.spark.ml.param.shared.HasStandardization

getStandardization, standardization

Methods inherited from interface org.apache.spark.ml.param.shared.HasWeightCol

getWeightCol, weightCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasSolver

getSolver

Methods inherited from interface org.apache.spark.ml.param.shared.HasAggregationDepth

aggregationDepth, getAggregationDepth

Methods inherited from interface org.apache.spark.ml.param.shared.HasLoss

getLoss

Methods inherited from interface org.apache.spark.ml.util.MLWritable

save

Methods inherited from interface org.apache.spark.internal.Logging

initializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Method Detail

read

public static MLReader<LinearRegressionModel> read()

load

public static LinearRegressionModel load(String path)

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

coefficients

public Vector coefficients()

intercept

public double intercept()

scale

public double scale()

numFeatures

public int numFeatures()

Description copied from class: PredictionModel

Returns the number of features the model was trained on. If unknown, returns -1

Overrides:

numFeatures in class PredictionModel<Vector,LinearRegressionModel>

summary

public LinearRegressionTrainingSummary summary()

Gets summary (e.g. residuals, mse, r-squared ) of model on training set. An exception is thrown if trainingSummary == None.

Returns:

(undocumented)

hasSummary

public boolean hasSummary()

Indicates whether a training summary exists for this model instance.

evaluate

public LinearRegressionSummary evaluate(Dataset<?> dataset)

Evaluates the model on a test dataset.

Parameters:

dataset - Test dataset to evaluate model on.

Returns:

(undocumented)

predict

public double predict(Vector features)

Description copied from class: PredictionModel

Predict label for the given features. This method is used to implement transform() and output predictionCol.

Specified by:

predict in class PredictionModel<Vector,LinearRegressionModel>

Parameters:

features - (undocumented)

Returns:

(undocumented)

copy

public LinearRegressionModel copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class Model<LinearRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

write

public GeneralMLWriter write()

Returns a GeneralMLWriter instance for this ML instance.

For LinearRegressionModel, this does NOT currently save the training summary. An option to save summary may be added in the future.

This also does not save the parent currently.

Specified by:

write in interface GeneralMLWritable

Specified by:

write in interface MLWritable

Returns:

(undocumented)