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(wish)list of probabilistic regressors to implement or to interface #32
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To note: Anyone can implement these however the high-level interface for composition/reduction needs to be discussed as well as interfacing Bayesian toolboxes |
yes, indeed:
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Oh and can I suggest adding some baselines? e.g. Gaussian with mean = sample mean, variance = sample var? |
Regarding Bayes, perhaps it's premature to look at this at all, without thinking carefully about a Bayesian mlr interface - since the issue with priors is potentially also of relevance in Bayesian classifiers, or Bayesian [any method]. |
and, obviously, any suggestions for the wishlist are welcome too |
That's a special case of two methods already there:
Though I agree it probably should be a "special" baseline with its own name, perhaps "the" baseline. I made a special "baseline" section. |
In line with "one feature, one issue" principle (which @RaphaelS1 mentioned in communication elsewhere) - should this be split in individual issues, and the list moved to wiki? |
If we split this into "one feature, one issue", now it will bloat the issue tracker. Let's split it once we actually finish the design and start implementing learners |
ok, let me know when. Just trying to comply with local best practice conventions. |
A wishlist for probabilistic regression methods to implement or interface.
Number of stars at the end is estimated difficulty or time investment.
GLM
KRR aka Gaussian process regression
CDE
Tree-based
Neural networks
Bayesian toolboxes
Pipeline elements for target transformation
Composite techniques, reduction to deterministic regression
Ensembling type pipeline elements and compositors
baselines
Other reduction from/to probabilistic regression
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