I used the cross validator tool for tuning the parameter. My code is here:
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.tuning import ParamGridBuilder, CrossValidator
from pyspark.ml.evaluation import BinaryClassificationEvaluator
reg=100.0
lr=LogisticRegression(maxIter=500)
paramGrid = ParamGridBuilder().addGrid(lr.regParam,
[0.02,0.01,0.2,0.1,1.0,2.0,10.0,15.0,20.0,100.0]).addGrid(lr.elasticNetParam,
[0.0, 0.5, 1.0]).build()
crossval = CrossValidator(estimator=lr,
estimatorParamMaps=paramGrid,
evaluator=BinaryClassificationEvaluator(),
numFolds=3)
model=crossval.fit(data_train_df)
And finally predicted the values:
prediction = model.transform(data_test_df)
prediction.show()
+-----+----------+
|label|prediction|
+-----+----------+
| 1.0| 1.0|
| 1.0| 1.0|
| 1.0| 1.0|
| 1.0| 1.0|
| 1.0| 1.0|
| 0.0| 1.0|
| 0.0| 1.0|
| 0.0| 1.0|
| 0.0| 1.0|
| 0.0| 1.0|
| 0.0| 1.0|
| 0.0| 1.0|
| 0.0| 1.0|
| 0.0| 1.0|
| 0.0| 1.0|
| 0.0| 1.0|
| 0.0| 1.0|
+-----+----------+
Why am I getting the wrong predictions?
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