🌐 Mailman 3 December 2018 - scikit-learn - python.org

Does random forest work if there are very few features?
by lampahome Feb. 19, 2019

Feb. 19, 2019

I read doc and know tree-based model is determined by entropy or gini impurity. When model try to create leaf nodes, it will determine based on the feature, right? Ex: I have 2 features A,B, and I divide it with A. So I have left and right nodes based on A. It should have the best shape if I create nodes based on A, right? Now if I have 100 estimators but I only have two features, do I have different trees which are all based on feature A? or the shape of trees based on A are all the same cuz they were created by feature A? thx

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Next Sprint
by Andreas Mueller Jan. 10, 2019

Jan. 10, 2019

Hey folks. Are there any plans for a next sprint (in Paris/Europe?)? OpenML would like to join us next time and I think that would be cool. But they (and I ;) need some advance planning. I also have some funding that I could use for this as well. Cheers, Andy

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Draft of a Scikit-learn governance document
by Andreas Mueller Dec. 27, 2018

Dec. 27, 2018

Hi all. I just posted a proposal for a scikit-learn governance document as a PR: https://github.com/scikit-learn/scikit-learn/pull/12878 The core devs discussed this already to some degrees but I think it would be great to involve the greater community in finalizing this. Any feedback is welcome. Cheers, Andy

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How to grab subsets from train sets when bootstrap=False in RF regressor?
by lampahome Dec. 27, 2018

Dec. 27, 2018

As title RF regressor decide a tree by grabing part of train data aka bootstrap. If set bootstrap=False, how would the model grab data? The reason I'm interesting is when I set it to False, it makes the mse and mae down, that's means False is better.

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Any way to tune the parameters better than GridSearchCV?
by lampahome Dec. 25, 2018

Dec. 25, 2018

Take random forest as example, if I give estimator from 10 to 10000(10, 100, 1000, 10000) into grid search. Based on the result, I found estimator=100 is the best, but I don't know lower or greater than 100 is better. How should I decide? brute force or any tools better than GridSearchCV? thx

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Re: [scikit-learn] How to keep a model running in memory?
by Roland Hochmuth Dec. 20, 2018

Dec. 20, 2018

Hi Liam, I would suggest start out by taking a look at the gRPC quickstart for Python at, https://grpc.io/docs/quickstart/python.html and then modifying that example to do what you would like. The Flask server would launch the separate process using multiprocessing. The model process would create a gRPC service endpoint. The Flask server would wait for the model process to start and then establish a gRPC connection as a client to the gRPC service endpoint of the model process. The gRPC service of the model process would have methods, such as trainModel or getModelStatus, … When an http request occurs on the Flask http server, the server would then invoke the gRPC methods in the model process. I hope that helps. Regards --Roland From: Liam Geron <liam(a)chatdesk.com> Date: Thursday, December 20, 2018 at 9:53 AM To: Roland Hochmuth <rhochmuth(a)alteryx.com> Cc: Scikit-learn mailing list <scikit-learn(a)python.org> Subject: Re: [scikit-learn] How to keep a model running in memory? Hi Roland, Thanks for the suggestion! I'll certainly look into gRPC or similar frameworks. Currently we have multiprocessing, but it's not used to that same extent. How would the second process have a sort of "listener" to respond to incoming requests if it is running persistently? Thanks so much for the help. Best, Liam On Thu, Dec 20, 2018 at 11:12 AM Roland Hochmuth <rhochmuth(a)alteryx.com<mailto:rhochmuth@alteryx.com>> wrote: Hi Liam, Not sure I have the complete context for what you are trying to do, but have you considered using Python multiprocessing to start a separate process? The lifecycle of that process could start when the Flask server starts-up or on the first request. The separate process would load and run the model. Depending on what you would like to do, some form of IPC mechanism, such as gRPC could be used to control or get updates from the model process. Regards --Roland From: scikit-learn <scikit-learn-bounces+rhochmuth=alteryx.com(a)python.org<mailto:alteryx.com@python.org>> on behalf of Aneto <aneto(a)chatdesk.com<mailto:aneto@chatdesk.com>> Reply-To: Scikit-learn mailing list <scikit-learn(a)python.org<mailto:scikit-learn@python.org>> Date: Thursday, December 20, 2018 at 8:21 AM To: "scikit-learn(a)python.org<mailto:scikit-learn@python.org>" <scikit-learn(a)python.org<mailto:scikit-learn@python.org>> Cc: Liam Geron <liam(a)chatdesk.com<mailto:liam@chatdesk.com>> Subject: [scikit-learn] How to keep a model running in memory? Hi scikit learn community, We currently use scikit-learn for a model that generates predictions on a server endpoint. We would like to keep the model running in memory instead of having to re-load the model for every new request that comes in to the server. Can you please point us in the right direction for this? Any tutorials or examples. In case it's helpful, we use Flask for our web server. Thank you! Aneto

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Submission
by 이인규 Dec. 20, 2018

Dec. 20, 2018

Leon LEE Leefrance79(a)gmail.com Skype: leefrance7979

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How to keep a model running in memory?
by Aneto Dec. 20, 2018

Dec. 20, 2018

Hi scikit learn community, We currently use scikit-learn for a model that generates predictions on a server endpoint. We would like to keep the model running in memory instead of having to re-load the model for every new request that comes in to the server. Can you please point us in the right direction for this? Any tutorials or examples. In case it's helpful, we use Flask for our web server. Thank you! Aneto

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time complexity of tree-based model?
by lampahome Dec. 20, 2018

Dec. 20, 2018

I do some benchmark in my experiments and I almost use ensemble-based regressor. What is the time complexity if I use random forest regressor? Assume I only set variable * estimators=100* and others doesn't enter. thx

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ANN Dirty_cat: learning on dirty categories
by Gael Varoquaux Dec. 19, 2018

Dec. 19, 2018

Hi scikit-learn friends, As you might have seen on twitter, my lab -with a few friends- has embarked on research to ease machine on "dirty data". We are experimenting on new encoding methods for non-curated string categories. For this, we are developing a small software project called "dirty_cat": https://dirty-cat.github.io/stable/ dirty_cat is a test bed for new ideas of "dirty categories". It is a research project, though we still try to do decent software engineering :). Rather than contributing to existing codebases (as the great categorical-encoding project in scikit-learn-contrib), we spanned it out in a separate software project to have the freedom to try out ideas that we might give up after gaining insight. We hope that it is a useful tool: if you have non-curated string categories, please give it a try. Understanding what works and what does not is important to know what to consolidate. Hopefully one day we can develop a tool that is of wide-enough interest that it can go in scikit-learn-contrib, or maybe even scikit-learn. Also, if you have suggestions of publicly available databases that we try it upon, we would love to hear from you. Cheers, Gaël PS: if you want to work on dirty-data problems in Paris as a post-doc or an engineer, send me a line

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