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Showing content from https://github.com/alteryx/evalml below:

alteryx/evalml: EvalML is an AutoML library written in python.

EvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.

Key Functionality

Install from PyPI:

or from the conda-forge channel on conda:

conda install -c conda-forge evalml

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
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')
from evalml.automl import AutoMLSearch
automl = AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary')
automl.search()
Get best pipeline and predict on new data
pipeline = automl.best_pipeline
pipeline.predict(X_test)

Read more about EvalML on our documentation page:

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

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.


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