I'm developing a Spark project in Scala. I want to create MLlib models like linear regression, naive bayes, gradient boosting regression etc.
Here what I want to try generating these models with different combinations by using their parameters. For example gradient boosting regression takes num_iters, maxDepth, learning_rate and maxBins as parameter. For now I specified some parameter ranges for this algorithm:
num_iters -> List.range(3,10)
maxDepth -> List.range(5,20)
maxBins -> List.range(25,50)
learning_rate -> List(0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1)
I searched lots of sources but I didn't find any solution.
What I want to know is how can I specify these parameter's ranges logically.