Statistical Classification
This procedure helps identify which category a new subject belongs to
The model is trained on instances whose category membership is known, and then uses the same algorithm to determine the category for the new observation.

Credit scoring is one of the most well-known examples of statistical classification. A credit score is a metric based on the analysis of credit files of a person to prove their credit-worthiness. It is used to classify applicants for credit into 'good' and 'bad' risk classes. Credit scoring is used by banks, government departments, and insurance, mobile phone, digital finance, and other companies.
BENEFITS OF STATISTICAL CLASSIFICATION:
Eliminates the human factor when deciding what category an item should be assigned to
Non-specialist users can make decisions according to score-based applications
All criteria used in statistical classification can be identified according to the organization's business needs
ILLUSTRATIVE EXAMPLES
Case study on ICO classification
At the preliminary analysis stage, we collected data on 1200 ICO projects and determined factors that influence whether the project raised the desired funds or not. Then we taught the model to recognize the 'good' projects we were interested in for further research. Classification helped us determine the most relevant object of study and concentrate on it.
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Statistical classification in Digital Health
We conducted research for a $100M investment fund. One of our stages was to divide all the countries into 'good' and 'bad' countries for investment. We identified factors that influence healthcare system efficiency and defined an investment attractiveness threshold. Statistical classification allowed us to identify the 'good' countries for further research.
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