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954 prediction explanations doc #981

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merged 25 commits into from Jul 28, 2020
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freddyaboulton
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@freddyaboulton freddyaboulton commented Jul 27, 2020

Pull Request Description

Fixes #954 by adding explain_prediction to the api reference and by adding example usage to the Model Understanding tutorial in the user guide.

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After creating the pull request: in order to pass the release_notes_updated check you will need to update the "Future Release" section of docs/source/release_notes.rst to include this pull request by adding :pr:123.

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codecov bot commented Jul 27, 2020

Codecov Report

Merging #981 into main will increase coverage by 0.00%.
The diff coverage is 100.00%.

Impacted file tree graph

@@           Coverage Diff           @@
##             main     #981   +/-   ##
=======================================
  Coverage   99.85%   99.85%           
=======================================
  Files         178      179    +1     
  Lines        9282     9283    +1     
=======================================
+ Hits         9269     9270    +1     
  Misses         13       13           
Impacted Files Coverage Δ
...alml/pipelines/prediction_explanations/__init__.py 100.00% <100.00%> (ø)

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@freddyaboulton freddyaboulton marked this pull request as ready for review Jul 27, 2020
@freddyaboulton freddyaboulton requested review from dsherry and jeremyliweishih and removed request for dsherry and jeremyliweishih Jul 27, 2020
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@angela97lin angela97lin left a comment

Well-explained! LGTM :D

Not blocking but do we need the evalml/tests/pipeline_tests/explanations_tests/__init__.py empty file?

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freddyaboulton commented Jul 27, 2020

@angela97lin Thanks! Good point, I'll delete in this PR!

@freddyaboulton freddyaboulton merged commit c828bed into main Jul 28, 2020
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@freddyaboulton freddyaboulton deleted the 954-prediction-explanations-doc branch Jul 28, 2020
"\n",
"In the example below, we explain the prediction for the third data point in the data set. We see that the `worst concave points` feature increased the estimated probability that the tumor is malignant by 20% while the `worst radius` feature decreased the probability the tumor is malignant by 5%.\n",
"\n",
"The interpretation of the table is the same for regression problems - but the SHAP value now corresponds to the change in the estimated value of the dependent variable rather than a change in probability. For multiclass classification problems, a table will be output for each possible class."
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@dsherry dsherry Jul 28, 2020

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@freddyaboulton I just checked this out. This looks great! My only thought was this paragraph could go after the example since its not directly connected to the example.

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@freddyaboulton freddyaboulton Jul 28, 2020

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Thanks @dsherry ! I like the suggestion and I'll make the change in #986

@angela97lin angela97lin mentioned this pull request Jul 31, 2020
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Write tutorial of prediction explanations feature
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