evalml 0.44.0

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

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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:

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[update_checker]"

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

Built at Alteryx Innovation Labs