Metadata-Version: 2.1
Name: evalml
Version: 0.11.2
Summary: EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.
Home-page: https://www.alteryx.com/innovation-labs
Author: Alteryx, Inc.
Author-email: support@featurelabs.com
License: UNKNOWN
Description: <p align="center">
        <img width=50% src="https://github.com/featurelabs/evalml/blob/main/docs/source/images/evalml_logo.png?raw=true" alt="Featuretools" />
        </p>
        
        [![CircleCI](https://circleci.com/gh/FeatureLabs/evalml.svg?style=svg&circle-token=9e0ce5e5f2db05f96fe92238fcde6d13963188b6)](https://circleci.com/gh/FeatureLabs/evalml)
        [![codecov](https://codecov.io/gh/featurelabs/evalml/branch/main/graph/badge.svg?token=JDc0Ib7kYL)](https://codecov.io/gh/featurelabs/evalml)
        
        EvalML is an AutoML library to build optimized machine learning pipelines for domain-specific objective functions.
        
        **Key Functionality**
        
        * **Domain-specific** - Includes repository of domain-specific objective functions and interface to define your own
        * **End-to-end** - Constructs and optimizes pipelines that include imputation, feature selection, and a variety of modeling techniques
        * **Data Checks** - Carefully cross-validates to prevent overfitting and warns you if training and testing results diverge
        
        ## Install
        ```shell
        pip install evalml --index-url https://install.featurelabs.com/<KEY>
        ```
        
        ## Quick Start
        
        #### Define objective
        ```python
        from evalml import AutoMLSearch
        from evalml.objectives import FraudCost
        
        
        fraud_objective = FraudCost(
            retry_percentage=.5,
            interchange_fee=.02,
            fraud_payout_percentage=.75,
            amount_col="amount"
        )
        ```
        
        #### Run automl
        ```python
        automl = AutoMLSearch(problem_type='binary', objective=fraud_objective,
                             max_pipelines=3)
        
        automl.search(X_train, y_train)
        ```
        
        #### See all pipeline ranks
        ```python
        automl.rankings
        ```
        
        #### Get best pipeline and predict on new data
        
        ```python
        pipeline = automl.best_pipeline
        pipeline.predict(X_test)
        ```
        
        ## Next Steps
        
        * [Configuring AutoClassifer]()
        * [Defining your own objective functions]()
        * [API Reference]()
        
        Read more about EvalML in our [Documentation](https://evalml.featurelabs.com).
        
        ## Built at Alteryx Innovation Labs
        <a href="https://www.alteryx.com/innovation-labs">
            <img src="docs/source/images/alteryx_innovation_labs.png" alt="Alteryx Innovation Labs" />
        </a>
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
