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LIBSVM ( http://www.csie.ntu.edu.tw/~cjlin/libsvm/ ). Update of the LIBSVM library for support vector machine algorithms (version 3.12, April 2012) [C - SVC, Epsilon-SVR, nu - SVR]. The calculations are faster. The attributes can be normalized or not. They were automatically normalized previously. LIBCVM ( http://c2inet.sce.ntu.edu.sg/ivor/cvm.html ; version 2.2). Incorporation of the LIBCVM library. Two methods are available: CVM and BVM (Core Vector Machine and Ball Vector Machine). The dezscriptors can be normalized or not. TR-IRLS ( http://autonlab.org/autonweb/10538 ). Update of the TR-IRLS library, for the logistic regression on large dataset (large number of predictive attributes) [last available version – 2006/05/08]. The deviance is automatically provided. The display of the regression coefficients is more precise (higher number of decimals). The user can tune the learning algorithms, especially the stopping rules. SPARSE DATA FILE. Tanagra can handle sparse data file format n

Model deployment is a crucial task of the data mining process. In the supervised learning, it can be the applying of the predictive model on new unlabeled cases. We have already described this task for various tools (e.g. Tanagra, Sipina, Spad, R). They have as common feature the use of the same tool for the model construction and the model deployment. In this tutorial, we describe a process where we do not use the same tool for the model construction and the model deployment. This is only possible if (1) the model is described in a standard format, (2) the tool which used for the deployment can handle both the database with unlabeled instances and the model. Here, we use the PMML standard description for the sharing of the model, and the PDI-CE ( Pentaho Data Integration Community Edition ) for the applying of the model on the unseen cases. We create a decision tree with various tools such as SIPINA, KNIME or RAPIDMINER; we export the model in the PMML format; then, we use PDI-CE for