Designed for domain experts, TADA by MyDataModels is a predictive modeling software that helps professionals use their Small Data to enhance their business with a light, easy to set up tool. It provides fast and usable results providing a predictive modeling solution. It’s not a problem if you’re not a data scientist. Based on the (r)evolutionary ZGP Engine, TADA is designed for business experts with no skills in coding nor data sciences.
Have your small datasets team up with predictive modeling to boost up your business
Set up meaningful datasets in a snap
Build and run machine learning models on any devices and platforms throught our powerful web-based pre-processing features: missing values management, stratified sampling, time series processing as well as automated variable reduction.
Easily train high performance models
Easily train and run high performance expressive models without writing a single line of code. Within a few minutes, deliver high-end predictions for your business thanks to our modeling capability for binary, multi-classification and regression.
Optimize processes for speed and scalability
Create compact predictive models with 3 to 7 variables, operating on any accessibles devices to generate a predictive model within minutes. Runs seamlessly in cloud and desktop environments.
Deploy and embed light and scalable auto ML predictive models for local insights and on-the-go permanent efficiency, through a call to a REST API endpoint (coming soon) or export your model’s code (Java, C++) into your application.
Shift from days to a few hours into building ad hoc effective models with our 40% reduced time automated data preparation.
Get outcomes from your data without programming or machine learning skills needed and without any training requirement for yourself.
Benefit from a lifetime free account to boost your business with the power of MyDataModels technology.
Optimize your time with explainable and understandable models made of easy-to-read formulas.
Achieve unrivalled performances on Small Data thanks to ZGP, our unique mathematical expression engine inspired by evolutionary algorithms.
Turn your data into insights in a snap on any platform (cloud, desktop, mobile, edge). Create effective automated models without code conversion.
ZGP : AI and mathematics at the service of your DATA.
ZGP combines two main fields of today’s AI: Symbolic Regression and Evolutionary Programming, to reach Zoetrope Genetic Programming achievement. It creates simple mathematical expressions that are particularly good at predicting or classifying Small Data. When most of today’s solutions take hours and hours (and a large amount of data) to produce decent result, ZGP produces much better models at a much faster pace.
After 10 years of research in AI, we continue to innovate.
We have now partnered with major research institutes (INRIA) to accelerate our research. We continue to invest massively in research and have built partnership with some of the most renowned mathematicians and researchers in the field. Like our algorithms, we evolve!
Multiple modeling & discriminating capacities
Binary classification, Regression and Multi-class classification modeling are available. The modeling algorithm is able to consider a large number of variables during analysis and automatically select a minimal subset comprised of the most useful variables.
Minimalistic & efficient
Models produced are minimalistic in the sense of having a minimum reliance upon larger quantities of independent variables. Models have a peak maximum efficiency when employing 3 to 7 independent variables. The algorithm is able to discern physical signals in small amounts of data, i.e very few rows
Understandable & insightful
Models produced are in the form of a human readable mathematical equation which can be deployed in computing languages (Java, C++). Having specific equations describing system behaviors enables both wide application and detailed exploration of underlying phenomena.
The algorithm produces accurate estimation of future model performance. The accuracy realized when a model is deployed closely matches that estimated by the modeling process