ATM: Scalable model selection and tuning

Auto Tune Models (ATM) is an AutoML system designed with ease of use in mind. In short, you give ATM a classification problem and a dataset as a CSV file, and ATM will try to build the best model it can. ATM is based on a paper of the same name, and the project is part of the Human-Data Interaction (HDI) Project at MIT.

To download ATM and get started quickly, head over to the setup section.

Background

AutoML systems attempt to automate part or all of the machine learning pipeline, from data cleaning to feature extraction to model selection and tuning. ATM focuses on the last part of the machine-learning pipeline: model selection and hyperparameter tuning.

Machine learning algorithms typically have a number of parameters (called hyperparameters) that must be chosen in order to define their behavior. ATM performs an intelligent search over the space of classification algorithms and hyperparameters in order to find the best model for a given prediction problem. Essentially, you provide a dataset with features and labels, and ATM does the rest.

Our goal: flexibility and power

Nearly every part of ATM is configurable. For example, you can specify which machine-learning algorithms ATM should try, which metrics it computes (such as F1 score and ROC/AUC), and which method it uses to search through the space of hyperparameters (using another HDI Project library, BTB). You can also constrain ATM to find the best model within a limited amount of time or by training a limited amount of total models.

ATM can be used locally or on a cloud-computing cluster with AWS. Currently, ATM only works with classification problems, but the project is under active development. If you like the project and would like to help out, check out our guide to contributing!