Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/16630#discussion_r100933207
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
---
@@ -1152,4 +1170,32 @@ class GeneralizedLinearRegressionTrainingSummary
private[regression] (
"No p-value available for this GeneralizedLinearRegressionModel")
}
}
+
+ /**
+ * Summary table with feature name, coefficient, standard error,
+ * tValue and pValue.
+ */
+ @Since("2.2.0")
+ lazy val summaryTable: DataFrame = {
+ if (isNormalSolver) {
+ var featureNames = featureName
+ var coefficients = model.coefficients.toArray
+ var idx = Array.range(0, coefficients.length)
+ if (model.getFitIntercept) {
+ featureNames = featureNames :+ "Intercept"
+ coefficients = coefficients :+ model.intercept
+ // Reorder so that intercept comes first
+ idx = (coefficients.length - 1) +: idx
+ }
+ val result = for (i <- idx.toSeq) yield
+ (featureNames(i), coefficients(i), coefficientStandardErrors(i),
tValues(i), pValues(i))
+
+ val spark = SparkSession.builder().getOrCreate()
+ import spark.implicits._
+ result.toDF("Feature", "Estimate", "StdError", "TValue",
"PValue").repartition(1)
--- End diff --
question: is "Estimate" the better term to use here as opposed to
"Coefficient"? Are there other libraries which use this specific term in this
case?
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