Master #125
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Master #125
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In my testing to date, the best paramters for the Gradient Boosting algorithm are: loss='deviance', n_estimators=100, learning_rate=0.1, max_depth=3, random_state=None, max_leaf_nodes=None These lead to the following results for the blind data: Optimized facies classification accuracy = 0.51 Optimized adjacent facies classification accuracy = 0.96
Combined code into MajorityVoting algorithm
Lots of things wrong with this, but uploading it for the sake of posterity if nothing else
This is ar4's submission with Gradient boosting. Some changes to params
just removed unnecessary comments
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Hello again Priyanka — and thank you for this new submission. It scores 0.623! I have not reproduced it yet, but usually this does not change things much. So congratulations on the really nice result! By the way, I want to draw your attention to this issue — I will be using the same approach to get a stochastic result for your notebook. Cheers! |
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By the way, this was the 100th scored entry — thanks for that! |
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Update: I have run the stochastic result. Your entry actually does slightly better than HouMath, so I have put it ahead in the table. Full result:
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Wow! Happy to see this.
…On Tue, Jan 24, 2017 at 9:38 PM, Matt Hall ***@***.***> wrote:
Update: I have run the stochastic result. Your entry actually does
slightly better than HouMath, so I have put it ahead in the table. Full
result:
[image: image]
<https://cloud.githubusercontent.com/assets/1692372/22255438/c4615438-e22d-11e6-8c3e-c69cdf26676e.png>
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Priyanka
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We only want to submit "Face_classification_SHPR_GradientBoost" for the contest (TRial number 2). Please ignore GradientBoosting.ipynb, GradientBoostingNew,MajorityVoting and RandomForest