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This tutorial complements the course material concerning the Kohonen map or Self-organizing map ( June 2017 ). In a first time, we try to highlight two important aspects of the approach: its ability to summarize the available information in a two-dimensional space; Its combination with a cluster analysis method for associating the topological representation (and the reading that one can do) to the interpretation of the groups obtained from the clustering algorithm. We use the R software and the “Kohonen” package (Wehrens et Buydens, 2007). In a second time, we carry out a comparative study of the quality of the partitioning with the one obtained with the K-means algorithm. We use an external evaluation i.e. we compare the clustering results with pre-established classes. This procedure is often used in research to evaluate the performance of clustering methods. It takes on its meaning when it is applied to artificial data where the true class membership is known. We use the K-Means and ...
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...
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