Adding beta warning for time series problems in AutoML #2118
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@@ Coverage Diff @@
## main #2118 +/- ##
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+ Coverage 100.0% 100.0% +0.1%
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Files 291 291
Lines 23697 23710 +13
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+ Hits 23687 23700 +13
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| "EvalML supports three common supervised ML problem types. The first is regression, where the target value to model is a continuous numeric value. Next are binary and multiclass classification, where the target value to model consists of two or more discrete values or categories. The choice of which supervised ML problem type is most appropriate depends on domain expertise and on how the model will be evaluated and used.\n", | ||
| "EvalML supports three common supervised ML problem types. The first is regression, where the target value to model is a continuous numeric value. Next are binary and multiclass classification, where the target value to model consists of two or more discrete values or categories. The choice of which supervised ML problem type is most appropriate depends on domain expertise and on how the model will be evaluated and used. \n", | ||
| "\n", | ||
| "EvalML is currently building support for supervised time series problems: time series regression, time series binary classification, and time series multiclass classification. While we've added some features to tackle these kinds of problems, our functionality is still being actively developed so please be mindful of that before using it. \n", |
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The issue originally mentions adding this blurb to the part where we detect problem types but I thought it made more sense here because we're already talking about the problem types we support.
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Apr 20, 2021
…skEngine`` #1975. - Added optional ``engine`` argument to ``AutoMLSearch`` #1975 - Added a warning about how time series support is still in beta when a user passes in a time series problem to ``AutoMLSearch`` #2118 - Added ``NaturalLanguageNaNDataCheck`` data check #2122 - Added ValueError to ``partial_dependence`` to prevent users from computing partial dependence on columns with all NaNs #2120 - Added standard deviation of cv scores to rankings table #2154 - Fixed ``BalancedClassificationDataCVSplit``, ``BalancedClassificationDataTVSplit``, and ``BalancedClassificationSampler`` to use ``minority:majority`` ratio instead of ``majority:minority`` #2077 - Fixed bug where two-way partial dependence plots with categorical variables were not working correctly #2117 - Fixed bug where ``hyperparameters`` were not displaying properly for pipelines with a list ``component_graph`` and duplicate components #2133 - Fixed bug where ``pipeline_parameters`` argument in ``AutoMLSearch`` was not applied to pipelines passed in as ``allowed_pipelines`` #2133 - Fixed bug where ``AutoMLSearch`` was not applying custom hyperparameters to pipelines with a list ``component_graph`` and duplicate components #2133 - Removed ``hyperparameter_ranges`` from Undersampler and renamed ``balanced_ratio`` to ``sampling_ratio`` for samplers #2113 - Renamed ``TARGET_BINARY_NOT_TWO_EXAMPLES_PER_CLASS`` data check message code to ``TARGET_MULTICLASS_NOT_TWO_EXAMPLES_PER_CLASS`` #2126 - Modified one-way partial dependence plots of categorical features to display data with a bar plot #2117 - Renamed ``score`` column for ``automl.rankings`` as ``mean_cv_score`` #2135 - Fixed ``conf.py`` file #2112 - Added a sentence to the automl user guide stating that our support for time series problems is still in beta. #2118 - Fixed documentation demos #2139 - Update test badge in README to use GitHub Actions #2150 - Fixed ``test_describe_pipeline`` for ``pandas`` ``v1.2.4`` #2129 - Added a GitHub Action for building the conda package #1870 #2148 .. warning:: - Renamed ``balanced_ratio`` to ``sampling_ratio`` for the ``BalancedClassificationDataCVSplit``, ``BalancedClassificationDataTVSplit``, ``BalancedClassficationSampler``, and Undersampler #2113 - Deleted the "errors" key from automl results #1975 - Deleted the ``raise_and_save_error_callback`` and the ``log_and_save_error_callback`` #1975 - Fixed ``BalancedClassificationDataCVSplit``, ``BalancedClassificationDataTVSplit``, and ``BalancedClassificationSampler`` to use minority:majority ratio instead of majority:minority #2077
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Pull Request Description
Fixes #2094
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.rstto include this pull request by adding :pr:123.