BART: improve accuracy and other minor fixes #5177
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- Coverage 78.11% 78.10% -0.02%
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- Hits 11061 11060 -1
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michaelosthege
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Apart from some variable renaming there seem to be some algorithmic changes.
I can't really review those..
Do you maybe want to list them in the PR description and linkt it from the RELEASE-NOTES.md?
ricardoV94
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Nov 17, 2021
RELEASE-NOTES.md
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| - Added partial dependence plots and individual conditional expectation plots [5091](https://github.com/pymc-devs/pymc3/pull/5091). | ||
| - Added linear response, increased number of trees fitted per step [5044](https://github.com/pymc-devs/pymc3/pull/5044). | ||
| - Added partial dependence plots and individual conditional expectation plots [5091](https://github.com/pymc-devs/pymc3/pull/5091). | ||
| - Modify how particle weights are computed. This imporves accuracy of the modeled function (see [5177](https://github.com/pymc-devs/pymc3/pull/5177)). |
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| - Modify how particle weights are computed. This imporves accuracy of the modeled function (see [5177](https://github.com/pymc-devs/pymc3/pull/5177)). | |
| - Modify how particle weights are computed. This improves accuracy of the modeled function (see [5177](https://github.com/pymc-devs/pymc3/pull/5177)). |
ricardoV94
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Nov 17, 2021
morganstrom
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Nov 17, 2021
* improve accuracy and other minor fixes * update release notes * fix typo
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The main change introduced in this PR is how particles weights are computed. All particles except for the first one (previous tree) are grown from scratch and re-weighted at each step. After all particles stop growing we compute the weights of all particles including the previous tree and normalize. This improve accuracy but at the same time makes the sampler "stickier", in a follow up PR I will introduce better "tree-movements" that should improve sampling.
Just to illustrate, this is an example of fitting a function with 5 covariables related to the output function and then progressively adding more covariables non-related to the output variable (pure noise).
and a partial dependant plot for 10 covariables (only the first 5 are related to the output)
