Great.
I like your second solution. But how can I make sure that cvModel holds
the best model overall (as opposed to the last one that was tired out
but the grid search)?
In addition, do you have an idea how to collect the average error of
each grid search (here 1x1x1)?
On 12/08/2016 08:59 μμ, Bryan Cutler wrote:
You will need to cast bestModel to include the MLWritable trait. The
class Model does not mix it in by default. For instance:
cvModel.bestModel.asInstanceOf[MLWritable].save("/my/path")
Alternatively, you could save the CV model directly, which takes care
of this
cvModel.save("/my/path")
On Fri, Aug 12, 2016 at 9:17 AM, Adamantios Corais
<adamantios.cor...@gmail.com <mailto:adamantios.cor...@gmail.com>> wrote:
Hi,
Assuming that I have run the following pipeline and have got the
best logistic regression model. How can I then save that model for
later use? The following command throws an error:
cvModel.bestModel.save("/my/path")
Also, is it possible to get the error (a collection of) for each
combination of parameters?
I am using spark 1.6.2
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml
<http://org.apache.spark.ml>.classification.LogisticRegression
import org.apache.spark.ml
<http://org.apache.spark.ml>.evaluation.BinaryClassificationEvaluator
import org.apache.spark.ml.tuning.{ParamGridBuilder , CrossValidator}
val lr = new LogisticRegression()
val pipeline = new Pipeline().
setStages(Array(lr))
val paramGrid = new ParamGridBuilder().
addGrid(lr.elasticNetParam , Array(0.1)).
addGrid(lr.maxIter , Array(10)).
addGrid(lr.regParam , Array(0.1)).
build()
val cv = new CrossValidator().
setEstimator(pipeline).
setEvaluator(new BinaryClassificationEvaluator).
setEstimatorParamMaps(paramGrid).
setNumFolds(2)
val cvModel = cv.
fit(training)
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