What: K2R is a regression algorithm that allows building models to predict categories or continuous variables.

Why: Traditionally, building robust predictive models required a lot of time and expertise, which prevented companies from using data mining as part of their every day business decisions. K2R makes it easy to build and deploy predictive models in the fraction of the time it takes using classical statistical tools.

How: K2R maps a set of descriptive attributes (model inputs) and target attributes (model output). It uses an algorithm patented by KXEN, which is a derivation of a principle described by V. Vapnik as “Structured Risk Minimization.” Instead of looking for the best performance on a known dataset, K2R automatically finds the best compromise between quality and robustness. The resulting models are expressed as a polynomial expression of the input numbers. The only element specified by the user is the polynomial degree. To improve modeling speed, K2R can also build multi-target models.

Benefits for the business user: K2R allows the business user to easily build and understand advanced predictive models without statistical knowledge. A model can be created in a matter of minutes. Two performance indicators describe model quality (Ki) and model reliability or the ability to produce similar on new data (Kr).

K2R graphically displays the individual variable contribution to the model, which helps to select the most important variables explaining a given business question. At the same time it avoids focusing on data that contains no information.

Models can directly be applied in a simulation mode for a single input dataset predicting the score for an individual business question in real time.

Benefits for the Data Mining expert: K2R frees time for Data Mining professionals to apply their expertise in areas where they add more value instead of spending several days to tune a model. K2R produces results within minutes (less than 15 seconds on a laptop with 50,000 lines and 20 variables).

Since KXEN models are robust, discovering outliers can lead to changing data such as taking more variables to build a model, or to identify specific behaviour like detecting fraud.

Benefits for the Integration specialist and IT: K2R is a single algorithm that requires no tuning and produces consistently very good results across many different applications (versus producing the best results in some specific situations). K2R is a universal solution for classification and regression problems.

Example:

Classification: Marketing campaign usage scenario

• Send a “Test mailing” to 5000 customers to offer them a new product,

• Collect the results of your test mailing to build a “Training” data set that associates things you know about customers prior to the mailing with the answers to your business question

• Train a model to “predict” the Yes/No answer

• Check the quality and robustness of your model (Ki, Kr)

• Apply the model to the 1,000,000 other customers in your database: this model associates each individual customer with a probability for answering Yes. Because you are using a robust model, the sum of probabilities is a good indicator of how many people will answer yes to this mail

• Send your mailing only to those customers with a high probability to respond positively, or use our built-in profit curves to optimize your return on the campaign

Example:

Regression: Dealer evaluation usage scenario

• Collect information about the past performance of your dealers two years ago and associate how much of your product they sold 1 year ago

• Train a model to predict how much a dealer will sell based on the available information

• Check the quality and robustness of the model (Ki, Kr)

• Apply the model to all of your dealers today: the model associates each dealer with an estimation of how many products he will sell,

• Sum up the estimates to predict how much you will sell next year. This is the base line for your sales forecast.