TADA brings immediate value to your business and research

Speed up any research by analyzing in depth your “Small Data”

TADA works well with already 100 data points while most solution need 10 times more to be efficient, you can have earlier results.

Save time and money in focusing on the most important variables

TADA’s Artificial Intelligence identifies the very few variables that have most impact on your business; so you can focus on your actions.

Discover unexpected correlations between variables

TADA’s Artificial Intelligence finds unexpected correlations that will dramatically change the way you organize your business or your research.

How does it work?

Have your datasets team up with predictive modeling to boost up your business in an easy way.

Ingest your Data in TADA

Your data must be stored in a Tabular way (excel or csv) where each column is a variable and each row is a data point. The first line must contain the name of the variables.

Once you have created a project and imported your data, you can check everything is fine. TADA automatically detects if there is any missing value and also detects automatically the type of the variables would be a number, a category or simply a text.

Select your Goal ​

You need then to define a Goal which is the variable you want to understand and predict in the future. There are 3 types of Goals: Binary (yes/no questions), Multi-Class (between 3 and 50 categories) & Regression (for numbers).

Define your Variable Set

Your dataset may contain variables that are useless to understand your Goal. TADA’s automatic variable reduction removes them and even shows you what are the most important variables for your Goal.

Also you may want to exclude one variable simply to see how the system would work without this information. You can do that manually.

Step number 4

Create your Models

TADA does that by generating hundreds of different predictive models and it selects the best of them. A predictive model “summarizes” the data: it tries to explain how your Goal can be deduced from all the other variables.

You can play with the parameters to have the first rough results in a few seconds and then create another model that will take several minutes to favor accuracy over speed.

TADA models are very accurate and at the same time simple enough to be understood by humans.

Data Science Advanced Metrics

Once the model is generated, we provide loads of metrics that measure how good it is: accurate, precise, etc…. These metrics are well known among data scientists.

Step number 5
Step number 6

Sensitivity Analysis ​

One of the most interesting features of TADA is called Sensitivity Analysis. It explains how a variable influences the final result. It helps you to understand with accuracy how your Goal behaves. Many key phenomenons have been understood thanks to this feature.

Explore all the prediction possibility

If you have generated an accurate model, you can then play with it. You can predict your goal based on all the values of the key variables. Most find it difficult to stop playing with it!

Step number 7

Deploy your predictive models

The models come as simple mathematical formulas that can be run directly in the TADA App. The models can be exported and deployed in code form (Python, C++ & Javascript) in your own application or by your IT team.

Start your AI path to performance

Test easily TADA with our test data here:

Solution for every professional

medical
Breast Cancer Prediction for Improved Diagnosis
Is it possible, thanks to machine learning, to improve breast cancer prediction? Can we be more accurate in diagnosing whether a cell extracted from the breast is a cancer cell?..
breast cancer
it
Machine Learning for IoT applications
Is it possible to train and run predictive models based on historical Small Data in a small memory footprint and then embed it within an IoT device?..
Iot
manufacturing
Failure Prediction
Is it possible to avoid partially or even totally industry downtimes? Can they be anticipated by using machine learning? Can the machines be repaired before they break down?..
failure prediction
medical
Pathology prediction for patient orientation
What if it was possible to help Emergency Call Centers Professionals to make a quick diagnosis based on a few questions, thanks to a machine learning tool? Could we help doctors and nurses diminish their diagnosis time?..
pathology-prediction
medical
Cardiovascular Disease Prediction
How could a general practitioner evaluate the risk of one of his patients having a heart attack within the next 24 hours?..
cardiovascular disease prediction
Solution

Use cases

aviation
Transportation
Real estate
Real Estate
public app
Public Services
marketing icon
Marketing
IT
manufacturing
Industry
medical
Healthcare
bank and services
Finance & Services

Powered by our core technology, theZGP Engine

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.

ZGP engine
Representation of various data

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