Enable Thresholding for Time Series Binary#3140
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ParthivNaresh merged 22 commits intomainfrom Dec 15, 2021
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## main #3140 +/- ##
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- Coverage 99.7% 99.7% -0.0%
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Files 318 318
Lines 30908 30948 +40
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+ Hits 30804 30843 +39
- Misses 104 105 +1
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# Conflicts: # evalml/pipelines/time_series_pipeline_base.py # evalml/tests/automl_tests/test_automl_utils.py # evalml/tests/pipeline_tests/test_time_series_pipeline.py
ParthivNaresh
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christopherbunn,
chukarsten,
eccabay and
freddyaboulton
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freddyaboulton
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Dec 10, 2021
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@ParthivNaresh Looks good to me! Thank you for making this change. The one thing I want to square away before merge is whether we should be tuning on forecast_horizon number of obs or use predict_proba_in_sample.
| automl_config.optimize_thresholds | ||
| and pipeline.can_tune_threshold_with_objective(threshold_tuning_objective) | ||
| ): | ||
| test_size_ = ( |
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I'm wondering if we should use predict_proba_in_sample rather than predict_proba.
My thoughts are:
- The target is known during search so we don't have to worry about the forecast horizon
- forecast horizon is probably less than 20% of the data and usually will be small I think. I wonder if that's enough data to find a good threshold.
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| def __init__(self, parameters, random_seed=0): | ||
| def __init__( | ||
| self, parameters, custom_name=None, component_graph=None, random_seed=0 |
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Just a cosmetic change right?
…teryx/evalml into EnableThresholdingForBinaryTS
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Fixes #3095