Changelog¶

Future Releases

v0.5.1 Nov. 15, 2019

Enhancements
- Added basic outlier detection guardrail #151
- Added basic ID column guardrail #135
- Added support for unlimited pipelines with a max_time limit #70
- Updated .readthedocs.yaml to successfully build #188
Fixes
- Removed MSLE from default additional objectives #203
- Fixed random_state passed in pipelines #204
- Fixed slow down in RFRegressor #206
Changes
- Pulled information for describe_pipeline from pipeline’s new describe method #190
- Refactored pipelines #108
- Removed guardrails from Auto(*) #202, #208
Documentation Changes
- Updated documentation to show max_time enhancements #189
- Updated release instructions for RTD #193
- Added contributing instructions #213

v0.5.0 Oct. 29, 2019

Enhancements
- Added basic one hot encoding #73
- Use enums for model_type #110
- Support for splitting regression datasets #112
- Auto-infer multiclass classification #99
- Added support for other units in max_time #125
- Detect highly null columns #121
- Added additional regression objectives #100
Fixes
- Reordered describe_pipeline #94
- Added type check for model_type #109
- Fixed s units when setting string max_time #132
- Fix objectives not appearing in API documentation #150
Changes
- Reorganized tests #93
- Moved logging to its own module #119
- Show progress bar history #111
- Using cloudpickle instead of pickle to allow unloading of custom objectives #113
- Removed render.py #154
Documentation Changes
- Update release instructions #140
- Include additional_objectives parameter #124
- Added Changelog #136
Testing Changes
- Code coverage #90
- Added CircleCI tests for other Python versions #104
- Added doc notebooks as tests #139
- Test metadata for CircleCI and 2 core parallelism #137

v0.4.1 Sep. 16, 2019

Enhancements
- Added AutoML for classification and regressor using Autobase and Skopt #7 #9
- Implemented standard classification and regression metrics #7
- Added logistic regression, random forest, and XGBoost pipelines #7
- Implemented support for custom objectives #15
- Feature importance for pipelines #18
- Serialization for pipelines #19
- Allow fitting on objectives for optimal threshold #27
- Added detect label leakage #31
- Implemented callbacks #42
- Allow for multiclass classification #21
- Added support for additional objectives #79
Fixes
- Fixed feature selection in pipelines #13
- Made random_seed usage consistent #45
Documentation Changes
- Documentation Changes
- Added docstrings #6
- Created notebooks for docs #6
- Initialized readthedocs EvalML #6
- Added favicon #38
Testing Changes
- Added testing for loading data #39

v0.2.0 Aug. 13, 2019

v0.1.0 July. 31, 2019