evalml 0.11.2

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

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

pip install evalml --index-url https://install.featurelabs.com/<KEY>

Quick Start

Define objective

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

automl = AutoMLSearch(problem_type='binary', objective=fraud_objective,
                     max_pipelines=3)

automl.search(X_train, y_train)

See all pipeline ranks

automl.rankings

Get best pipeline and predict on new data

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.

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