Hi Luca,
Could you please explain how can do this randomized trees in scikit-learn?
So you suggest I should be using Random forest?
On Fri, Feb 5, 2016 at 4:13 PM, Luca Puggini <lucapug...@gmail.com> wrote:
> 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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