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Regression (_e.g._ linear regression, logistic regression, poisson regression, etc) is a very important in machine learning. Many problems can be formulated in the form of (regularized) regression.
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In general, Bayesian methods trade in greater flexibility for computational expense.
Bayesian regularization algorithms extend the classical Ridge Regression with:
- [ ] a more data driven appro…
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I know that skforecast uses bootstrapping to estimate prediction uncertainty. How is it different from regressors such as gaussian / bayesian ridge which already gives estimation uncertainty? How can …
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Hi!
Thanks for an awesome and easy-to-use package!
I'm currently doing some marketing mix forecasting and was wondering, is their any way (by parameter or transform) to ensure that learned coeff…
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> By having a wild thought on start a career on Artificial intelligence, started today to do a tiny research on how to study AI. It turns out it consist of a shit black hole of knowledges, which means…
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here are a few missing ones as reported by Fatameh:
unknown journal: {'nlmid': '0400722', 'medlineAbbreviation': 'Med Arh', 'isoabbreviation': 'Med Arh', 'title': 'Medicinski arhiv'}
unknown journ…
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#### Problem location
https://www.mlpack.org/doc/stable/python_documentation.html#bayesian_linear_regression
#### Description of problem
When I am trying out mlpack under Python b…
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Some initial ideas of additional calibration approaches to try out that use a similar approach to variational Bayesian Monte Carlo (VBMC) of fitting a Gaussian process surrogate to evaluations of the …
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I found `smallrnaseq` depends on scikit-learn==0.19.1, while the new version of which is 0.22.
I can install 0.22 version without error, but unfortunately, 0.19.1 seems not compatible in my computer.…
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Dear teacher and TAs, I wonder if there is a syllabus for this course? I couldn't find one in course.pku.edu.cn. Thank you!