Mailman 3 June 2019 - scikit-learn

fit before partial_fit ?
by lampahome June 10, 2019

June 10, 2019

I tried MiniBatchKMeans with two order: fit -> partial_fit partial_fit -> partial_fit The clustering results are different what's their difference?

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Tune parameters when I need to load data segment by segment?
by lampahome June 9, 2019

June 9, 2019

As title I have one huge data to load, so I need to train it incrementally. So I load data segment by segment and train segment by segment like: MiniBatchKMeans. In that condition, how to tune parameters? tune the first part of data or every part of data?

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Google code reviews
by Andreas Mueller June 7, 2019

June 7, 2019

Hi All. What do you think of https://www.pullrequest.com/googleserve/? It's sponsored code reviews. Could be interesting, right? Best, Andy

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Normalization in ridge regression when there is no intercept
by ahmetcik June 7, 2019

June 7, 2019

Hello everyone, I have just recognized that when using ridge regression without an intercept no normalization is performed even if the argument "normalize" is set to True. Though it is, of course, no problem to manually normalize the input matrix X I have become curious if there was a special reason to not normalize the data, e.g. the columns of X scaled (but not centered to have mean zero) to have unit norm such that their lengths do not affect the outcome. Thanks in advance! Emre

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Any way to tune threshold of Birch rather than GridSearchCV?
by lampahome June 5, 2019

June 5, 2019

I use Birch to cluster my data and my data is kind of time-series data. I don't know the actually cluster numbers and need to read large data(online learning), so I choose Birch rather than MiniKmeans. When I read it, I found the critical parameters might be branching_factor and threshold, and threshold will affect my cluster numbers obviously! Any way to estimate the suitable threshold of Birch? Any paper suggestion is ok. thx

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Only a few days left to submit! -- 2019 John Hunter Excellence in Plotting Contest
by Nelle Varoquaux June 3, 2019

June 3, 2019

Hi everybody, There are only a few days left to submit to the 2019 John Hunter Excellence in Plotting Contest! If you're interested in participating, note that you have until June 8th to prepare your submission. In memory of John Hunter, we are pleased to be reviving the SciPy John Hunter Excellence in Plotting Competition for 2019. This open competition aims to highlight the importance of data visualization to scientific progress and showcase the capabilities of open source software. Participants are invited to submit scientific plots to be judged by a panel. The winning entries will be announced and displayed at the conference. John Hunter’s family and NumFocus are graciously sponsoring cash prizes for the winners in the following amounts: - 1st prize: $1000 - 2nd prize: $750 - 3rd prize: $500 - Entries must be submitted by June, 8th to the form at https://goo.gl/forms/cFTB3FUBrMPfQ7Vz1 - Winners will be announced at Scipy 2019 in Austin, TX. - Participants do not need to attend the Scipy conference. - Entries may take the definition of “visualization” rather broadly. Entries may be, for example, a traditional printed plot, an interactive visualization for the web, or an animation. - Source code for the plot must be provided, in the form of Python code and/or a Jupyter notebook, along with a rendering of the plot in a widely used format. This may be, for example, PDF for print, standalone HTML and Javascript for an interactive plot, or MPEG-4 for a video. If the original data can not be shared for reasons of size or licensing, "fake" data may be substituted, along with an image of the plot using real data. - Each entry must include a 300-500 word abstract describing the plot and its importance for a general scientific audience. - Entries will be judged on their clarity, innovation and aesthetics, but most importantly for their effectiveness in communicating a real-world problem. Entrants are encouraged to submit plots that were used during the course of research or work, rather than merely being hypothetical. - SciPy reserves the right to display any and all entries, whether prize-winning or not, at the conference, use in any materials or on its website, with attribution to the original author(s). SciPy John Hunter Excellence in Plotting Competition Co-Chairs Hannah Aizenman Thomas Caswell Madicken Munk Nelle Varoquaux

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