EvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.
Key Functionality
- Automation - Makes machine learning easier. Avoid training and tuning models by hand. Includes data quality checks, cross-validation and more.
- Data Checks - Catches and warns of problems with your data and problem setup before modeling.
- End-to-end - Constructs and optimizes pipelines that include state-of-the-art preprocessing, feature engineering, feature selection, and a variety of modeling techniques.
- Model Understanding - Provides tools to understand and introspect on models, to learn how they'll behave in your problem domain.
- Domain-specific - Includes repository of domain-specific objective functions and an interface to define your own.
Installation
Install from PyPI:
pip install evalml
or from the conda-forge channel on conda:
conda install -c conda-forge evalml
Add-ons
Update checker - Receive automatic notifications of new Woodwork releases
PyPI:
pip install "evalml[updater]"
Conda:
conda install -c conda-forge alteryx-open-src-update-checker
Start
Load and split example data
import evalml
X, y = evalml.demos.load_breast_cancer()
X_train, X_test, y_train, y_test = evalml.preprocessing.split_data(X, y, problem_type='binary')
Run AutoML
from evalml.automl import AutoMLSearch
automl = AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary')
automl.search()
View pipeline rankings
automl.rankings
Get best pipeline and predict on new data
pipeline = automl.best_pipeline
pipeline.predict(X_test)
Next Steps
Read more about EvalML on our documentation page:
- Installation and getting started.
- Tutorials on how to use EvalML.
- User guide which describes EvalML's features.
- Full API reference
Support
The EvalML community is happy to provide support to users of EvalML. Project support can be found in four places depending on the type of question:
- For usage questions, use Stack Overflow with the
evalmltag. - For bugs, issues, or feature requests start a Github issue.
- For discussion regarding development on the core library, use Slack.
- For everything else, the core developers can be reached by email at open_source_support@alteryx.com
Built at Alteryx
EvalML is an open source project built by Alteryx. To see the other open source projects we’re working on visit Alteryx Open Source. If building impactful data science pipelines is important to you or your business, please get in touch.
8 maintainers
Release files for evalml 0.84.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| evalml-0.84.0.tar.gz | 6.3 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| evalml-0.84.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 12.9 MB
Release files / evalml-0.84.0.tar.gz
| Download URL | evalml-0.84.0.tar.gz |
|---|---|
| Size | 6.3 MB |
| Tags | Source |
|
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Yes |
| Uploaded via |
twine/5.0.0 CPython/3.12.3
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Release files / evalml-0.84.0-py3-none-any.whl
| Download URL | evalml-0.84.0-py3-none-any.whl |
|---|---|
| Size | 6.6 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/5.0.0 CPython/3.12.3
|
