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.
Install from PyPI:
pip install evalml
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[update_checker]"
Conda:
conda install -c conda-forge alteryx-open-src-update-checker
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()
automl.rankings
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