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Raphael Sourty raphaelsty Renault Group Toulouse https://raphaelsty.github.io PhD Student @ IRIT and Renault

raphaelsty/mkb 34

Knowledge Base Embedding By Cooperative Knowledge Distillation

Certificat-sciences-des-donnees-bigdata/Module-sensibilisation 20

Retrouvez ici les contenus à étudier en autonomie du module sensibilisation.

MaxHalford/idao-2020-qualifier 16

Solution of team "Data O Plomo" to the qualification phase of the 2020 edition of the International Data Analysis Olympiad (IDAO)

AdilZouitine/outfit 13

:dress: Tidy up your machine learning experiments

raphaelsty/abayes 8

Autoregressive Bayesian linear model

MaxHalford/idao-2020-final 7

Solution of team "Data O Plomo" to the final phase of the 2020 edition of the International Data Analysis Olympiad (IDAO)

raphaelsty/M5-Forecasting-Accuracy 2

Deploying machine learning easily

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push eventonline-ml/river

smastelini

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rerun notebook

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smastelini

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Add coverage tests

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pull request commentonline-ml/river

Make tree splitters public

The failing tests are related to classifier chains. I believe they were recently updated. Pinging @MaxHalford.

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pull request commentonline-ml/river

Make tree splitters public

Hey @MaxHalford and @jacobmontiel, I added a page in the user guides that is a walkthrough in the tree module. I talked about the different tree models available, model inspection, memory management, and splitters. Hope that can help the users.

Your feedback is welcomed.

(Later today I'll add some basic coverage tests for the splitters)

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Make Metric inherit from base.Base

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init track logic

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finish benchmark logic

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fix style

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fix tests

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Creme -> River

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Add the DenStream clustering algorithm

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Merge pull request #471 from online-ml/benchmarks Benchmarks refactoring

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Add exemple sentence classification

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Update sentence classification example

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add no_learn parameter to pipeline prediction to avoid updating transformers

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add tests verifying scaler updating

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s/no_learn/learn_unsupervised

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Merge pull request #503 from Styren/add-no-learn Add no learn parameter to pipeline transformations

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parametrize tests for proba and score_one

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lint

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Merge pull request #505 from Styren/add-no-learn Parametrize tests for proba-methods

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issue openedscikit-multiflow/scikit-multiflow

decr_cutpoint should be initialized as float("-inf") so the two_side_option can work?

https://github.com/scikit-multiflow/scikit-multiflow/blob/d073a706b5006cba2584761286b7fa17e74e87be/src/skmultiflow/drift_detection/hddm_w.py#L84

https://github.com/scikit-multiflow/scikit-multiflow/blob/d073a706b5006cba2584761286b7fa17e74e87be/src/skmultiflow/drift_detection/hddm_w.py#L220

As is discribed in the title.

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pull request commentonline-ml/river

Add four internal metrics for incremental clustering (Cohesion, SSQ, Separation and Silhouette)

Yes you're probably right @jacobmontiel. I agree that a table would be very helpful :)

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pull request commentonline-ml/river

Add four internal metrics for incremental clustering (Cohesion, SSQ, Separation and Silhouette)

My opinion would be to put these clustering metrics in the cluster module.

This could be confusing to the user and would break the existing structure. I vote to keep them inside metrics. Also, some metrics (external) are used for both classification and clustering.

We can improve the documentation of this module. For example, the scikit-learn documentation has a table:

image

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created repositoryMaxHalford/bbc-weather-honolulu

☀️ Measuring the accuracy of BBC weather in Honolulu, USA

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