I want to do a very simple cross validation using LogisticRegression. Here is my code:
logreg = LogisticRegression(labelCol = "churn", featuresCol = "features") pipeline = Pipeline(stages = [logreg]) paramGrid = ParamGridBuilder().addGrid(logreg.regParam, [.1, .01]).build() crossval = CrossValidator( estimator = pipeline, estimatorParamMaps = paramGrid, evaluator = BinaryClassificationEvaluator(), numFolds = 2) bestLogReg = crossval.fit(df_train)
When I run this, I get the following error on
bestLogReg = crossval.fit(df_train):
IllegalArgumentException: label does not exist. Available: features, churn, CrossValidator_764038c00edc_rand, rawPrediction, probability, prediction
Here is my
df_train dataset's schema:
root |-- features: vector (nullable = true) |-- churn: integer (nullable = true)
I have fit this to a LogisticRegression before and it predicts fine.
Can you help me figure out what I did wrong?