DB Tsai created SPARK-5127:
------------------------------
Summary: Fixed overflow when there are outliers in data in
Logistic Regression
Key: SPARK-5127
URL: https://issues.apache.org/jira/browse/SPARK-5127
Project: Spark
Issue Type: Bug
Components: MLlib
Reporter: DB Tsai
gradientMultiplier = (1.0 / (1.0 + math.exp(margin))) - label
However, the first part of gradientMultiplier will be suffered from overflow if
there are samples far away from hyperplane, and this happens when there are
outliers in data. As a result, we use the equivalent formula but more
numerically stable.
val gradientMultiplier =
if (margin > 0.0) {
val temp = math.exp(-margin)
temp / (1.0 + temp) - label
} else {
1.0 / (1.0 + math.exp(margin)) - label
}
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
