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Gael Varoquaux proslijedio/la je Tweet
The first paper on SciPy, after 19 years - feels so good to finally see this published! Credit goes to all SciPy contributors over all those years, amazing team effort! And thanks to Nature Methods for making it open access!https://twitter.com/AllisonDoerr14/status/1224399749220007936 …
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Gael Varoquaux proslijedio/la je Tweet
There's something that many people in science and technology don't seem to get: you can either work on better methods, testing them on well-understood problems, or use well-understood methods for new problems. Innovating in multiple layers at the same time just produces noise.
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Most questions are not about "prediction". But machine learning is about estimating functions that approximate conditional expectations / probability. We need to get better at integrating it in our scientific inference pipelines. For more, push me to write a paper on this. 8/8
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For thousands of data points and large dimensionality, linear models (ridge) are needed. But applying them without thousands of data points (as I tried for many years) is hazardous. Get more data, change the question (eg analyze across cohorts). 7/8
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For thousands of data points and moderate dimensionality (99% of cases), gradient-boosted trees provide the necessary regression model https://scikit-learn.org/stable/modules/ensemble.html#histogram-based-gradient-boosting … They are robust to data distribution and support missing values (even outside MAR settings https://arxiv.org/abs/1902.06931 ) 6/8
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If there are less than a thousand data points, all but the simple statistical question can and will be gamed (sometimes unconsciously), partly for lack of model selection. An example in neuroimaging https://www.biorxiv.org/content/10.1101/843193v1 … I no longer trust such endeavors, including mines. 5/8
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We need non-parametric model selection and testing, that do not break if the model is wrong. Cross-validation and permutation importance provide these, once we have chosen input (endogenous) and output (exogenous) variables. 4/8
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We need weakly-parametric models that can fit data as raw as possible, without relying on non-testable assumptions. Machine learning provides these, and tree-based models need little data transformations. 3/8
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First, analytic variability is a killer. eg in "standard" analysis for brain mapping https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.24603 …, for machine learning in brain imaging https://www.sciencedirect.com/science/article/abs/pii/S1053811917305311 … or more generally in "hypothesis driven" statistical testing https://go.gale.com/ps/anonymous?id=GALE|A389260653&linkaccess=abs&issn=00030996 … 2/8
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Even for science and medical applications, I am becoming weary of fine statistical modeling efforts, and believe that we should standardize on a handful of powerful and robust methods. An opinionated thread to give context for https://twitter.com/GaelVaroquaux/status/1223305762350534657 … 1/8
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Gael Varoquaux proslijedio/la je Tweet
Scientific Python lecture notes http://scipy-lectures.org/
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Gael Varoquaux proslijedio/la je Tweet
Happy birthday
@scikit_learn !! Ten years from the first release and still young!pic.twitter.com/DXkTnoOdF9
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Gael Varoquaux proslijedio/la je Tweet
Pandas 1.0 is here! * Read the release notes: https://pandas.pydata.org/pandas-docs/version/1.0/whatsnew/v1.0.0.html … * Read the blogpost reflecting on what 1.0 means to our project: https://dev.pandas.io/pandas-blog/pandas-10.html … * Install with conda / PyPI: https://pandas.pydata.org/pandas-docs/version/1.0.0/getting_started/install.html … Thanks to our 300+ contributors to this release.
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Some papers share copy-pasted paragraphs: authors are submitting multiple variants to increase their chances. While beneficial for the individual, this behavior is disastrous for the group: fatigue of the selection process & dilution of publications. We should penalize it. 3/3
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Many papers on same topic, with similar (but differing) contributions. Reading 6 papers on 1-shot learning (or multi-view clustering) brings boredom
. On the opposite a good paper on less trendy topics stimulates me
.
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Screening 40 (!) papers as senior program committee for
@IJCAIconf The sheer volume of submissions is exhausting and time consuming. As a result human factors will influence my decisions
.
Some thoughts
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Gael Varoquaux proslijedio/la je Tweet
10 PR already submitted and the first one just merged! Having fun at the Paris
@scikit_learn Sprint of the Decade!pic.twitter.com/OrUefbQ3hE
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Gael Varoquaux proslijedio/la je Tweet
Attention OHBM attendees who are also parents: Please let us know of you plan to attend
#OHBM2020 with your children. We are planning to provide some creative and engaging activities for your little ones. Please RT and share with other OHBM parents.https://twitter.com/OHBM/status/1220808338717528068 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Gael Varoquaux proslijedio/la je Tweet
"What's wrong with computational notebooks?" The 9 pain points this user study highlighted me, both as a notebook user and from our experience running Kaggle's hosted notebook implementation (commentary in thread) http://web.eecs.utk.edu/~azh/blog/notebookpainpoints.html …
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Gael Varoquaux proslijedio/la je Tweet
Rereading Lykken: Lykken, D. T. (1991). What’s Wrong with Psychology Anyway? In D. Cicchetti & W. Grove (Eds.), Thinking clearly about psychology (Vol. 1, pp. 3–39). What a remarkable paper. A few things that stood out to me.
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