Classification notebook

[alex-hse-repository]

Before submitting (must do checklist)

• Did you read the contribution guide?
• Did you update the docs? We use Numpy format for all the methods and classes.
• Did you write any new necessary tests?
• Did you update the CHANGELOG?

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[codecov-commenter]

Codecov Report

Merging #997 (b6ab64f) into master (5d9103f) will decrease coverage by 0.07%.
The diff coverage is 45.45%.

@@            Coverage Diff             @@
##           master     #997      +/-   ##
==========================================
- Coverage   85.82%   85.74%   -0.08%     
==========================================
  Files         162      162              
  Lines        8605     8614       +9     
==========================================
+ Hits         7385     7386       +1     
- Misses       1220     1228       +8     

Impacted Files	Coverage Δ
etna/experimental/classification/predictability.py	81.25% <45.45%> (-18.75%)	⬇️
etna/auto/runner/utils.py	50.00% <0.00%> (-50.00%)	⬇️
etna/models/nn/deepar.py	98.93% <0.00%> (-0.02%)	⬇️
etna/models/nn/tft.py	99.10% <0.00%> (-0.01%)	⬇️

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[julia-shenshina]

add Raises block please

[alex-hse-repository]

Done

[julia-shenshina]

add Classification notebook to tutorials section in docs and CHANGELOG please

[review-notebook-app]

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julia-shenshina commented on 2022-11-22T15:39:15Z
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why do we need this plot? as I can see, it is not connected with false-positive statement because there are no intersections over segments 🤔

alex-hse-repository commented on 2022-11-22T16:37:53Z
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What do you mean? These are the segments with the good metrics which were predicted to be bad(so false positives). Table above(metrics.tail(10)) shows metrics for these segments

[alex-hse-repository]

What do you mean? These are the segments with the good metrics which were predicted to be bad(so false positives). Table above(metrics.tail(10)) shows metrics for these segments

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