@keyword_only
@since('1.6.0')
def setParams(self, featuresCol='features', labelCol='label', predictionCol='prediction', fitIntercept=True, maxIter=100, tol=1e-06, censorCol='censor', quantileProbabilities=list([0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]), quantilesCol=None, aggregationDepth=2):
    '\n        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",                   fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor",                   quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99],                   quantilesCol=None, aggregationDepth=2):\n        '
    kwargs = self._input_kwargs
    return self._set(**kwargs)
setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",                   fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor",                   quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99],                   quantilesCol=None, aggregationDepth=2):
@keyword_only
@since('1.6.0')
def setParams(self, featuresCol='features', labelCol='label', predictionCol='prediction', fitIntercept=True, maxIter=100, tol=1e-06, censorCol='censor', quantileProbabilities=list([0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99]), quantilesCol=None, aggregationDepth=2):
    '\n        \n        '
    kwargs = self._input_kwargs
    return self._set(**kwargs)