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Notes for Computer Science

Notes for Computer Science. October 10, 2015. Model ensembling is a very powerful technique to increase accuracy on a variety of ML tasks. In this article I will share my ensembling approaches for Kaggle Competitions. For the first part we look at creating ensembles from submission files. The second part will look at creating ensembles through stacked generalization/blending. I answer why ensembling … More KAGGLE ENSEMBLING GUIDE. Winning solution of Kaggle Higgs competition: what a single model can do?

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Notes for Computer Science | notes4computerscience.wordpress.com Reviews

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Notes for Computer Science. October 10, 2015. Model ensembling is a very powerful technique to increase accuracy on a variety of ML tasks. In this article I will share my ensembling approaches for Kaggle Competitions. For the first part we look at creating ensembles from submission files. The second part will look at creating ensembles through stacked generalization/blending. I answer why ensembling … More KAGGLE ENSEMBLING GUIDE. Winning solution of Kaggle Higgs competition: what a single model can do?

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Notes for Computer Science | notes4computerscience.wordpress.com Reviews

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Notes for Computer Science. October 10, 2015. Model ensembling is a very powerful technique to increase accuracy on a variety of ML tasks. In this article I will share my ensembling approaches for Kaggle Competitions. For the first part we look at creating ensembles from submission files. The second part will look at creating ensembles through stacked generalization/blending. I answer why ensembling … More KAGGLE ENSEMBLING GUIDE. Winning solution of Kaggle Higgs competition: what a single model can do?

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Glmnet Vignette – Notes for Computer Science

https://notes4computerscience.wordpress.com/2015/10/01/glmnet-vignette

Notes for Computer Science. October 1, 2015. October 1, 2015. Trevor Hastie and Junyang Qian. Stanford June 26, 2014. Glmnet is a package that fits a generalized linear model via penalized maximum likelihood. The regularization path is computed for the lasso or elasticnet penalty at a grid of values for the regularization parameter lambda. The algorithm is extremely fast, and can exploit sparsity in the input matrix. Solves the following problem. Over a grid of values of λ. Penalty is controlled by α.

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Notes for Computer Science – Page 2

https://notes4computerscience.wordpress.com/page/2

Notes for Computer Science. Profiling Top Kagglers: Owen Zhang, Currently #1 in the World. September 27, 2015. Next up in our series on top Kagglers is the #1: Owen Zhang(Zhonghua Zhang). Owen comes from an engineering background and currently works as the Chief Product Officer at DataRobot. Owen Q&A How did you start with Kaggle competitions? Back in 2011 I had just switched to analytics as a full time job (after several … More Profiling Top Kagglers: Owen Zhang, Currently #1 in the World.

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KAGGLE ENSEMBLING GUIDE – Notes for Computer Science

https://notes4computerscience.wordpress.com/2015/10/10/kaggle-ensembling-guide

Notes for Computer Science. October 10, 2015. October 10, 2015. Model ensembling is a very powerful technique to increase accuracy on a variety of ML tasks. In this article I will share my ensembling approaches for Kaggle Competitions. For the first part we look at creating ensembles from submission files. The second part will look at creating ensembles through stacked generalization/blending. This is how you win ML competitions: you take other peoples’ work and ensemble them together. Relay the signal m...

4

Top 10 R Packages to be a Kaggle Champion – Notes for Computer Science

https://notes4computerscience.wordpress.com/2015/09/27/top-10-r-packages-to-be-a-kaggle-champion

Notes for Computer Science. Top 10 R Packages to be a Kaggle Champion. September 27, 2015. September 27, 2015. Kaggle top ranker Xavier Conort shares insights on the 10 R Packages to Win Kaggle Competitions . Since R is widely being used even outside the data science community (such as by statisticians, actuaries, etc.), this list of top 10 powerful R packages might help you in more ways than you might think. Here are those 10 packages particularly powerful to build winning solutions:. Fill in your detai...

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Winning solution of Kaggle Higgs competition: what a single model can do? – Notes for Computer Science

https://notes4computerscience.wordpress.com/2015/10/07/winning-solution-of-kaggle-higgs-competition-what-a-single-model-can-do

Notes for Computer Science. Winning solution of Kaggle Higgs competition: what a single model can do? October 7, 2015. October 7, 2015. Winning solution of Kaggle Higgs competition: what a single model can do? This blog is for describing the winning solution of the Kaggle Higgs competition. It has the public score of 3.75 and the private score of 3.73 which has ranked at 26th. This solution uses a single classifier. Github link to the source code. Eta = 0.01 (small shrinkage). Shrinkage too slow and trai...

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Notes for Computer Science

Notes for Computer Science. October 10, 2015. Model ensembling is a very powerful technique to increase accuracy on a variety of ML tasks. In this article I will share my ensembling approaches for Kaggle Competitions. For the first part we look at creating ensembles from submission files. The second part will look at creating ensembles through stacked generalization/blending. I answer why ensembling … More KAGGLE ENSEMBLING GUIDE. Winning solution of Kaggle Higgs competition: what a single model can do?

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