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I am the main author of "Domain adaptation for regression under Beer–Lambert’s law" and creator of the (unsupervised) domain adaptation extension of partial least squares regression called di-PLS that…
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### Feature description
Radial Basis Function Neural Networks (RBFNNs) are a type of neural network that combines elements of clustering and function approximation, making them powerful for both regr…
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According to [Intel site LightGBM should already have OneDAL support](https://www.intel.com/content/www/us/en/developer/articles/technical/improve-performance-xgboost-lightgbm-inference.html).
But …
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Now, with the Hermite-functions and a CI properly set up, the core functionality of the package can be implemented:
The Robust Weighted Least Squares approach to Fourier Transform as described in
…
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How to apply StatsModels for partial least squares regression?
There is PLSRegression in sklearn, but some statistical results are missing, compared to StatsModels.GLM
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Would be cool to support logistic regression, so we could implement the unconditional logit estimator described in [Stamann et al:](https://www.econstor.eu/bitstream/10419/145837/1/VfS_2016_pid_6909.p…
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I like Veusz fitting for the flexible expression, but it seems sometimes give strange results even in some simple linear regression (as below). Are these intended behaviors or bugs?
If this behavio…
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Sklearn implements [Partial Least Squares Regression](http://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html) (PLSR) but a very common use for this algorithm i…
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Dear alegiac95,
Thank you for your toolbox "Imaging-transcriptomics".
I have …
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It would be nice to support the [Benchopt](https://github.com/benchopt/benchopt) problem suite, which is also available in Python:
- [ ] Ordinary Least Squares
- [ ] Non-Negative Least Squares
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