gtolomei commented on issue #14456:
URL: https://github.com/apache/spark/pull/14456#issuecomment-618493192
Thanks for your reply Sean!
My setting is as follows: I'm running PySpark 2.4.5 remotely over Google
Colab.
As per the way in which I print out the result, I'm using the following
function:
```
def summarize_all_models(cv_models):
for k, models in enumerate(cv_models):
print("*************** Fold #{:d} ***************\n".format(k+1))
for i, m in enumerate(models):
print("--- Model #{:d} out of {:d} ---".format(i+1, len(models)))
print("\tParameters: lambda=[{:.3f}]; alpha=[{:.3f}]
".format(m.stages[-1]._java_obj.getRegParam(),
m.stages[-1]._java_obj.getElasticNetParam()))
print("\tRMSE:
{:.3f}\n".format(m.stages[-1].summary.rootMeanSquaredError))
print("***************************************\n")
```
I call the above function as follows:
```
summarize_all_models(cv_model.subModels)
```
where `cv_model` is the result returning from the k-fold cross validation.
Thanks again for your help!
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