To me the score is not so low. The model is slightly over fitting. Try to
repeat the same process with extremely randomized trees instead of random
forest and try to keep a low depth.
On Fri 5 Feb 2016 at 16:01 muhammad waseem <m.waseem.ah...@gmail.com> wrote:
> Dear All,
> I am trying to train my model using Scikit-learn's Random forest
> (Regression) and have tried to use GridSearch with Cross-validation (CV=5)
> to tune hyperparameters. I fixed n_estimators =2000 for all cases. Below
> are the few searches that I performed.
>
> 1) max_features :[1,3,5], max_depth :[1,5,10,15],
> min_samples_split:[2,6,8,10], bootstrap:[True, False]
> The best were max_features=5, max_depth = 15, min_samples_split:10,
> bootstrap=True
> Best score = 0.8724
>
> Then I searched close to the parameters that were best;
> 2) max_features :[3,5,6], max_depth :[10,20,30,40],
> min_samples_split:[8,16,20,24], bootstrap:[True, False]
> The best were max_features=5, max_depth = 30, min_samples_split:20,
> bootstrap=True
> Best score = 0.8722
>
> Again, I searched close to the parameters that were best;
> 3) max_features :[2,4,6], max_depth :[25,35,40,50],
> min_samples_split:[22,28,34,40], bootstrap:[True, False]
>
> The best were max_features=4, max_depth = 25, min_samples_split:22,
> bootstrap=True
> Best score = 0.8725
>
> Then I used GridSearch among the best parameters found in the above runs
> and found the best on as max_features=4, max_depth = 15,
> min_samples_split:10,
> Best score = 0.8729
>
> Then I used these parameters to predict for an unknown dataset but got a
> very low score (around 0.72).
>
> My questions are; Am I doing the hyperparameter tuning correctly or I am
> missing something?
>
> 2) Why is my testing score very low as compared to my training and
> validation score and how can I improve it so that I get good predictions
> out of my model?
>
> Sorry, if these are basic questions as I am new to scikit-learn and ML.
>
> Thanks!
>
>
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