Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/14834#discussion_r79515040
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -508,11 +680,51 @@ object LogisticRegression extends
DefaultParamsReadable[LogisticRegression] {
@Since("1.4.0")
class LogisticRegressionModel private[spark] (
@Since("1.4.0") override val uid: String,
- @Since("2.0.0") val coefficients: Vector,
- @Since("1.3.0") val intercept: Double)
+ @Since("2.1.0") val coefficientMatrix: Matrix,
+ @Since("2.1.0") val interceptVector: Vector,
+ @Since("1.3.0") override val numClasses: Int,
--- End diff --
Actually that won't work under the current edge case behaviors. When the
labels are all `0.0` then the coefficient matrix will have only one row
regardless of multinomial or binomial. We could potentially change this
behavior though, as if we always assume there will be at minimum two classes.
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