Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/8564#discussion_r40055543
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala ---
@@ -298,11 +298,7 @@ class LinearRegressionModel private[ml] (
*/
// TODO: decide on a good name before exposing to public API
private[regression] def evaluate(dataset: DataFrame):
LinearRegressionSummary = {
- val t = udf { features: Vector => predict(features) }
- val predictionAndObservations = dataset
- .select(col($(labelCol)),
t(col($(featuresCol))).as($(predictionCol)))
-
- new LinearRegressionSummary(predictionAndObservations,
$(predictionCol), $(labelCol))
+ new LinearRegressionSummary(transform(dataset), $(predictionCol),
$(labelCol))
--- End diff --
So I mean doing a prediction on a dataset without a predictionCol defined
would cause errors no?
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