Embark on a fully automated machine learning experience free of hyperparameters. Whether you're working with relational or regular tabular data, Khiops eliminates the tedious tweaks (including feature engineering and variable encoding), allowing focus on data and model explainability.
Khiops makes machine learning on structured data easier, faster, and more intuitive than ever before, yet extremely sophisticated algorithms. This open source project offers an ultra-efficient and secure Auto ML pipeline, within everyone's reach!
A new lean Machine Learning experience to enhance the power of Data Scientists.
Khiops stands out for its singular formalism that offers significant advantages:
Automation: it automates ML pipeline tasks like data preparation, cleaning, variable selection, and aggregate extraction from relational data. Its hyperparameter-free approach prevents overfitting.
Interpretability: it is interpretable by design thanks to optimal encoding (grouping and discretization), explicit aggregates from relational data, and parsimonious training selecting a few variables.
Ultra scalable: The approach is hyperparameter-free, which avoids costly cross-validation and grid search. In addition, Khiops' C++ code is highly optimized and implements an efficient strategy for hardware adaptation.
Khiops is based on a breakthrough mathematical formalism which enables end-to-end ML pipeline without over-fitting or hyperparameters adjustment. No more time-consuming data preparation or manual feature engineering, even with relational data. The models learned are accurate and parsimonious, making them easy to interpret.
Khiops is a low-code Python library featuring an efficient Auto ML pipeline in a simple .fit() function. Sophisticated algorithms have never been so easy to use, thanks to its sklearn-like implementation. Khiops allows anyone to practice Machine Learning safely. You can analyze your models and better understand your data with minimal coding required.
The library has an interactive visualization tool that enables access to comprehensive preparation and modelization results directly from a notebook or a dedicated app. As a result, you won't have to write specific visualization codes to showcase your models. Additionally, Khiops offers a desktop version that allows you to use all the learning algorithms without needing to write any Python codes.
Khiops is a highly robust system designed for industrial usage, ensuring optimal functionality. The processing of its algorithms adjusts to the hardware resources available, allowing for seamless transitions between out-of-core and distributed calculations. Khiops adapts calculation time based on the size of your data. By using Khiops, you can reduce your expenses for cloud providers.