Have your datasets team up with predictive modeling to boost up your business in an easy way.
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. Quaartz 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.
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).
Your dataset may contain variables that are useless to understand your Goal. Quaartz 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.
Quaartz 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.
Quaartz models are very accurate and at the same time simple enough to be understood by humans.
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.
One of the most interesting features of Quaartz 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.
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!
The models come as simple mathematical formulas that can be run directly in the Quaartz App. The models can be exported and deployed in code form (Python, C++ & Javascript) in your own application or by your IT team.
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.
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