Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/10384#discussion_r48355484
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
---
@@ -23,15 +23,23 @@ import org.apache.spark.Logging
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary,
MultivariateOnlineSummarizer}
import org.apache.spark.sql.DataFrame
-
/**
* Evaluator for regression.
*
- * @param predictionAndObservations an RDD of (prediction, observation)
pairs.
+ * @param predictionAndObservations an RDD of (prediction, observation)
pairs,
+ * @param regThroughOrigin true if intercept is not included in linear
regression model
--- End diff --
@dbtsai It may be nitpicking, but `RegressionMetrics` class can be used for
any regression model and so `hasFitIntercept` doesn't make sense for all types
of regressors (as mentioned in other comments). Someone evaluating a decision
tree regression model might be confused by the parameter. I am not certain what
is best, so I will defer to others' opinions.
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