Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/4675#discussion_r24935325
--- Diff: docs/ml-guide.md ---
@@ -300,19 +302,21 @@ List<LabeledPoint> localTest = Lists.newArrayList(
new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5)));
-JavaSchemaRDD test = jsql.createDataFrame(jsc.parallelize(localTest),
LabeledPoint.class);
+DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest),
LabeledPoint.class);
// Make predictions on test documents using the Transformer.transform()
method.
// LogisticRegression.transform will only use the 'features' column.
-// Note that model2.transform() outputs a 'probability' column instead of
the usual 'score'
-// column since we renamed the lr.scoreCol parameter previously.
-model2.transform(test).registerAsTable("results");
-JavaSchemaRDD results =
- jsql.sql("SELECT features, label, probability, prediction FROM
results");
+// Note that model2.transform() outputs a 'myProbability' column instead
of the usual
+// 'probability' column since we renamed the lr.probabilityCol parameter
previously.
+model2.transform(test).registerTempTable("results");
+DataFrame results =
+ jsql.sql("SELECT features, label, myProbability, prediction FROM
results");
--- End diff --
With the DataFrame API, we don't need to call SQL now.
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