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author | Yanbo Liang <ybliang8@gmail.com> | 2016-02-22 23:37:09 -0800 |
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committer | Xiangrui Meng <meng@databricks.com> | 2016-02-22 23:37:09 -0800 |
commit | 72427c3e115daf06f7ad8aa50115a8e0da2c6d62 (patch) | |
tree | 4f193b6e3d4ffcd30b08149aa2faed5fe08bf1ac /python/pyspark/mllib/classification.py | |
parent | 4fd1993692d45a0da0289b8c7669cc1dc3fe0f2b (diff) | |
download | spark-72427c3e115daf06f7ad8aa50115a8e0da2c6d62.tar.gz spark-72427c3e115daf06f7ad8aa50115a8e0da2c6d62.tar.bz2 spark-72427c3e115daf06f7ad8aa50115a8e0da2c6d62.zip |
[SPARK-13429][MLLIB] Unify Logistic Regression convergence tolerance of ML & MLlib
## What changes were proposed in this pull request?
In order to provide better and consistent result, let's change the default value of MLlib ```LogisticRegressionWithLBFGS convergenceTol``` from ```1E-4``` to ```1E-6``` which will be equal to ML ```LogisticRegression```.
cc dbtsai
## How was the this patch tested?
unit tests
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #11299 from yanboliang/spark-13429.
Diffstat (limited to 'python/pyspark/mllib/classification.py')
-rw-r--r-- | python/pyspark/mllib/classification.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index b24592c379..b4d54ef61b 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -327,7 +327,7 @@ class LogisticRegressionWithLBFGS(object): @classmethod @since('1.2.0') def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType="l2", - intercept=False, corrections=10, tolerance=1e-4, validateData=True, numClasses=2): + intercept=False, corrections=10, tolerance=1e-6, validateData=True, numClasses=2): """ Train a logistic regression model on the given data. @@ -359,7 +359,7 @@ class LogisticRegressionWithLBFGS(object): (default: 10) :param tolerance: The convergence tolerance of iterations for L-BFGS. - (default: 1e-4) + (default: 1e-6) :param validateData: Boolean parameter which indicates if the algorithm should validate data before training. |