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Nowadays, much of modern personal computers (PC) have multicore processors. The computer operates as if it had multiple processors. Software and data mining algorithms must be modified in order to benefit of this new feature. Currently, few free tools exploit this opportunity because it is impossible to define a generic approach that would be valid regardless of the learning method used. We must modify each existing learning algorithm. For a given technique, decomposing an algorithm into elementary tasks that can execute in parallel is a research field in itself. In a second step, we must adopt a programming technology which is easy to implement. In this tutorial, I propose a technology based on threads for the induction of decision trees. It is well suited in our context for various reasons. (1) It is easy to program with the modern programming languages. (2) Threads can share information; they can also modify common objects. Efficient synchronization tools enable to avoid data corrup

The naive bayes classifier is a very popular approach even if it is (apparently) based on an unrealistic assumption: the distributions of the predictors are mutually independent conditionally to the values of the target attribute. The main reason of this popularity is that the method proved to be as accurate as the other well-known approaches such as linear discriminant analysis or logistic regression on the majority of the real dataset. But an obstacle to the utilization of the naive bayes classifier remains when we deal with a real problem. It seems that we cannot provide an explicit model for its deployment. The proposed representation by the PMML standard for instance is particularly unattractive. The interpretation of the model, especially the detection of the influence of each descriptor on the prediction of the classes is impossible. This assertion is not entirely true. We have showed in a previous tutorial that we can extract an explicit model from the naive bayes classifier i