@keyword_only
@since('1.4.0')
def setParams(self, featuresCol='features', labelCol='label', predictionCol='prediction', maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-06, fitIntercept=True, standardization=True, solver='auto', weightCol=None, aggregationDepth=2, loss='squaredError', epsilon=1.35):
    '\n        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",                   maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,                   standardization=True, solver="auto", weightCol=None, aggregationDepth=2,                   loss="squaredError", epsilon=1.35)\n        Sets params for linear regression.\n        '
    kwargs = self._input_kwargs
    return self._set(**kwargs)
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",                   maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,                   standardization=True, solver="auto", weightCol=None, aggregationDepth=2,                   loss="squaredError", epsilon=1.35)
Sets params for linear regression.
@keyword_only
@since('1.4.0')
def setParams(self, featuresCol='features', labelCol='label', predictionCol='prediction', maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-06, fitIntercept=True, standardization=True, solver='auto', weightCol=None, aggregationDepth=2, loss='squaredError', epsilon=1.35):
    '\n        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",                   maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,                   standardization=True, solver="auto", weightCol=None, aggregationDepth=2,                   loss="squaredError", epsilon=1.35)\n        Sets params for linear regression.\n        '
    kwargs = self._input_kwargs
    return self._set(**kwargs)