public class LinearRegressionModel extends RegressionModel<Vector,LinearRegressionModel> implements MLWritable
LinearRegression
.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.
|
Param<String> |
featuresCol()
Param for features column name.
|
String |
getFeaturesCol() |
String |
getLabelCol() |
String |
getPredictionCol() |
boolean |
hasSummary()
Indicates whether a training summary exists for this model instance.
|
double |
intercept() |
Param<String> |
labelCol()
Param for label column name.
|
static LinearRegressionModel |
load(String path) |
int |
numFeatures()
Returns the number of features the model was trained on.
|
Param<String> |
predictionCol()
Param for prediction column name.
|
static MLReader<LinearRegressionModel> |
read() |
LinearRegressionTrainingSummary |
summary()
Gets summary (e.g.
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
StructType |
validateAndTransformSchema(StructType schema,
boolean fitting,
DataType featuresDataType)
Validates and transforms the input schema with the provided param map.
|
Vector |
weights() |
MLWriter |
write()
Returns a
MLWriter instance for this ML instance. |
setFeaturesCol, setPredictionCol, transform, transformSchema
transform, transform, transform
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
save
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn, validateParams
toString
initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning
public static MLReader<LinearRegressionModel> read()
public static LinearRegressionModel load(String path)
public String uid()
Identifiable
uid
in interface Identifiable
public Vector coefficients()
public double intercept()
public Vector weights()
public int numFeatures()
PredictionModel
numFeatures
in class PredictionModel<Vector,LinearRegressionModel>
public LinearRegressionTrainingSummary summary()
trainingSummary == None
.public boolean hasSummary()
public LinearRegressionModel copy(ParamMap extra)
Params
copy
in interface Params
copy
in class Model<LinearRegressionModel>
extra
- (undocumented)defaultCopy()
public MLWriter write()
MLWriter
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.
write
in interface MLWritable
public StructType validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType)
schema
- input schemafitting
- whether this is in fittingfeaturesDataType
- SQL DataType for FeaturesType.
E.g., VectorUDT
for vector features.public Param<String> labelCol()
public String getLabelCol()
public Param<String> featuresCol()
public String getFeaturesCol()
public Param<String> predictionCol()
public String getPredictionCol()