Optmise deicision tree parameters
I have created a process which trains a decision tree model.
The optimise parameter operator was used to optimise this model.
Question: Does the optimise operator create the most satisfactory model based on the parameters you put into the operator?
i.e. if I put in min_leaf_size as min:1 and Max 15, the operator choses the most appropirate min_leaf_size?
Also, just theoretically, if my accuracy was 71.6% for my model ,what approaches would one take to improve their model? I understand each study is case specific, but are there any general rules?
thank you for taking the time to read this,
Sinead
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hello @sinead_bracken - yes Optimize Parameters cycles through all the permutations selected and chooses the parameters that result in the best performance. Note that adding additional permutations can be very resource-consuming as it's exponentially more cases to examine. If you open the Churn Modeling template there is actually a very good example of how these pieces all connect together.
Scott