@keyword_only
@since('2.0.0')
def setParams(self, labelCol='label', featuresCol='features', predictionCol='prediction', family='gaussian', link=None, fitIntercept=True, maxIter=25, tol=1e-06, regParam=0.0, weightCol=None, solver='irls', linkPredictionCol=None, variancePower=0.0, linkPower=None, offsetCol=None, aggregationDepth=2):
    '\n        setParams(self, labelCol="label", featuresCol="features", predictionCol="prediction",                   family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6,                   regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=None,                   variancePower=0.0, linkPower=None, offsetCol=None, aggregationDepth=2)\n        Sets params for generalized linear regression.\n        '
    kwargs = self._input_kwargs
    return self._set(**kwargs)
setParams(self, labelCol="label", featuresCol="features", predictionCol="prediction",                   family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6,                   regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=None,                   variancePower=0.0, linkPower=None, offsetCol=None, aggregationDepth=2)
Sets params for generalized linear regression.
@keyword_only
@since('2.0.0')
def setParams(self, labelCol='label', featuresCol='features', predictionCol='prediction', family='gaussian', link=None, fitIntercept=True, maxIter=25, tol=1e-06, regParam=0.0, weightCol=None, solver='irls', linkPredictionCol=None, variancePower=0.0, linkPower=None, offsetCol=None, aggregationDepth=2):
    '\n        setParams(self, labelCol="label", featuresCol="features", predictionCol="prediction",                   family="gaussian", link=None, fitIntercept=True, maxIter=25, tol=1e-6,                   regParam=0.0, weightCol=None, solver="irls", linkPredictionCol=None,                   variancePower=0.0, linkPower=None, offsetCol=None, aggregationDepth=2)\n        Sets params for generalized linear regression.\n        '
    kwargs = self._input_kwargs
    return self._set(**kwargs)