Awesome

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:

1. For usage questions, use Stack Overflow with the evalml tag.
2. For bugs, issues, or feature requests start a Github issue.
3. For discussion regarding development on the core library, use Slack.
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. 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.