Khiops: Orange Innovation’s AutoML Suite

A Complete ML Suite

The Khiops suite builds supervised models (classifiers and regressors) as well as basic and advanced unsupervised models (hierarchical coclusterings) for exploratory analysis. The suite also provides visualization tools to analyze and interpret the resulting models.

End-to-end AutoML

Khiops automatizes many time-consuming tasks including:

Feature engineering for large relational datasets
Discretization of numerical features and grouping of categorical ones
Decision tree creation
Feature selection in supervised models

Solid Theoretical Foundations

Khiops achieves an excellent compromise between accuracy, speed of learning and deployment, robustness and explainability. Its modeling algorithms are based on information theoretic principles and have been object of many peer-reviewed publications.

Resource Efficient

Khiops is implemented in C++ and makes the best use of your CPU cores, RAM and disk capacity. Khiops algorithms scale well with very large datasets by efficiently using disk storage when they do not fit entirely in RAM and by using multiple CPU cores.

Used in Real-World Applications

Khiops is a production-level ML suite that has been extensively used in real world applications: At Orange it is currently used for churn detection, fraud detection, sentiment analysis, appetency modeling and many others.

Integrable

Khiops runs natively on various Linux distributions, Windows systems as well as clusters. Several integration enablers are available, such as pyKhiops a complete Python API.