diff options
Diffstat (limited to 'python/pyspark')
| -rw-r--r-- | python/pyspark/ml/classification.py | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index 253af15cb5..29d1d203f2 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -79,12 +79,12 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti @keyword_only def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", - maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, + maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, threshold=0.5, thresholds=None, probabilityCol="probability", rawPredictionCol="rawPrediction", standardization=True, weightCol=None): """ __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ - maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \ + maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \ threshold=0.5, thresholds=None, probabilityCol="probability", \ rawPredictionCol="rawPrediction", standardization=True, weightCol=None) If the threshold and thresholds Params are both set, they must be equivalent. @@ -92,7 +92,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti super(LogisticRegression, self).__init__() self._java_obj = self._new_java_obj( "org.apache.spark.ml.classification.LogisticRegression", self.uid) - self._setDefault(maxIter=100, regParam=0.1, tol=1E-6, threshold=0.5) + self._setDefault(maxIter=100, regParam=0.0, tol=1E-6, threshold=0.5) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) self._checkThresholdConsistency() @@ -100,12 +100,12 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti @keyword_only @since("1.3.0") def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", - maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, + maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, threshold=0.5, thresholds=None, probabilityCol="probability", rawPredictionCol="rawPrediction", standardization=True, weightCol=None): """ setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ - maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \ + maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \ threshold=0.5, thresholds=None, probabilityCol="probability", \ rawPredictionCol="rawPrediction", standardization=True, weightCol=None) Sets params for logistic regression. |