eh-evalml 0.0.0
an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions
License: BSD-3-Clause
Requires Python: <4,>=3.9
Keywords: data science, machine learning, optimization, automl

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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 open_source_support@alteryx.com

## 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.

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