EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.
EvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.
- 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.
Install from PyPI
pip install evalml
Receive automatic notifications of new EvalML releases
pip install evalml[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()
View pipeline rankings
Get best pipeline and predict on new data
pipeline = automl.best_pipeline pipeline.predict(X_test)
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
Built at Alteryx Innovation Labs
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