EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.

Alteryx hosts two open-source projects for modeling.

Featuretools is a framework to perform automated feature engineering. It excels at transforming temporal and relational datasets into feature matrices for machine learning.

Compose is a tool for automated prediction engineering. It allows you to structure prediction problems and generate labels for supervised learning.

We’ve seen Featuretools and Compose enable users to easily combine multiple tables into transformed and aggregated features for machine learning, and to define time series supervised machine learning use-cases.

The question we then asked was: what happens next? How can users of Featuretools and Compose build machine learning models in a simple and flexible way?

We’re excited to announce that a new open-source project has joined the Alteryx open-source ecosystem. EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.

import evalml

## obtain features, a target and a problem type for that target
X, y = evalml.demos.load_breast_cancer()
problem_type = 'binary'
X_train, X_test, y_train, y_test = evalml.preprocessing.split_data(
    X, y, problem_type=problem_type, test_size=.2)

## perform a search across multiple pipelines and hyperparameters
automl = AutoMLSearch(X=x, y=y, problem_type=problem_type)
automl.search()

## the best pipeline is already refitted on the entire training data
best_pipeline = automl.best_pipeline
best_pipeline.predict(X_test)

EvalML’s AutoML search in action

EvalML provides a simple, unified interface for building machine learning models, using those models to generate insights and to make accurate predictions. EvalML provides access to multiple modeling libraries under the same API. EvalML supports a variety of supervised machine learning problem types including regression, binary classification and multiclass classification. Custom objective functions let users phrase their search for a model directly in terms of what they value. Above all we’ve aimed to make EvalML stable and performant, with ML performance testing on every release.

#automl #python #machine-learning #data-science