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https://github.com/alteryx/evalml
EvalML is an AutoML library written in python.
https://github.com/alteryx/evalml
automl data-science feature-engineering feature-selection hyperparameter-tuning machine-learning model-selection optimization
Last synced: 3 months ago
JSON representation
EvalML is an AutoML library written in python.
- Host: GitHub
- URL: https://github.com/alteryx/evalml
- Owner: alteryx
- License: bsd-3-clause
- Created: 2019-07-17T21:36:30.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2024-02-26T22:43:41.000Z (4 months ago)
- Last Synced: 2024-02-26T23:50:34.120Z (4 months ago)
- Topics: automl, data-science, feature-engineering, feature-selection, hyperparameter-tuning, machine-learning, model-selection, optimization
- Language: Python
- Homepage: https://evalml.alteryx.com
- Size: 16.4 MB
- Stars: 693
- Watchers: 23
- Forks: 84
- Open Issues: 312
-
Metadata Files:
- Readme: README.md
- Contributing: contributing.md
- License: LICENSE
Lists
- awesome-mlops - EvalML - A library that builds, optimizes, and evaluates ML pipelines using domain-specific functions. (AutoML)
- awesome-AutoML - EvalML
- awesome-llmops - EvalML - square) | (AutoML / Profiling)
- awesome-stars - alteryx/evalml - EvalML is an AutoML library written in python. (Python)
- awesome-open-source-mlops - EvalML - An open source python library for AutoML. (Scheduling)
- Awesome-LLMOps - EvalML - An open source python library for AutoML. (Profiling)
- awesome-stars - alteryx/evalml - EvalML is an AutoML library written in python. (Python)
README
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](https://pypi.org/project/evalml/):
```bash
pip install evalml
```
or from the conda-forge channel on [conda](https://anaconda.org/conda-forge/evalml):
```bash
conda install -c conda-forge evalml
```
### Add-ons
**Update checker** - Receive automatic notifications of new Woodwork releases
PyPI:
```bash
pip install "evalml[updater]"
```
Conda:
```
conda install -c conda-forge alteryx-open-src-update-checker
```
## Start
#### Load and split example data
```python
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
```python
from evalml.automl import AutoMLSearch
automl = AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary')
automl.search()
```
#### View pipeline rankings
```python
automl.rankings
```
#### Get best pipeline and predict on new data
```python
pipeline = automl.best_pipeline
pipeline.predict(X_test)
```
## Next Steps
Read more about EvalML on our [documentation page](https://evalml.alteryx.com/):
* [Installation](https://evalml.alteryx.com/en/stable/install.html) and [getting started](https://evalml.alteryx.com/en/stable/start.html).
* [Tutorials](https://evalml.alteryx.com/en/stable/tutorials.html) on how to use EvalML.
* [User guide](https://evalml.alteryx.com/en/stable/user_guide.html) which describes EvalML's features.
* Full [API reference](https://evalml.alteryx.com/en/stable/api_reference.html)
## 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:
1. For usage questions, use [Stack Overflow](https://stackoverflow.com/questions/tagged/evalml) with the `evalml` tag.
2. For bugs, issues, or feature requests start a [Github issue](https://github.com/alteryx/evalml/issues).
3. For discussion regarding development on the core library, use [Slack](https://join.slack.com/t/alteryx-oss/shared_invite/zt-182tyvuxv-NzIn6eiCEf8TBziuKp0bNA).
4. For everything else, the core developers can be reached by email at [email protected]
## Built at Alteryx
**EvalML** is an open source project built by [Alteryx](https://www.alteryx.com). To see the other open source projects we’re working on visit [Alteryx Open Source](https://www.alteryx.com/open-source). If building impactful data science pipelines is important to you or your business, please get in touch.
