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Creating reports with Tanagra

Reporting is a true test of differentiation between software data mining vocational and those derived from research. To a practitioner (eg research officer), it is important to be able to easily recover the result of his work in a process text or in a slideshow. The case becomes particularly interesting when it already has an output in spreadsheet format. Indeed the results are often presented in the form of various tables and possibly graphics. The ultimate is to define in advance models of relationships that are fed only at the end of the calculations and that can be printed directly. For the researcher who develops tools, it is all well and good, but this is absolutely not recoverable academically. I see very bad for me to propose an article in a magazine showing that I am able to automatically integrate 3D pie charts in a PDF file. Of Indeed, the tools developed by researchers often simply outputs text, certainly comprehensive, but not presentable in the state reports to be disseminated widely. The outputs of R or Weka are a good example.

Tanagra, created by a teacher researcher, follows the same approach. Nothing was initially planned for the reporting. And yet, paradoxically, he suggested in one of its menus (DIAGRAM / CREATE REPORT) a tool for creating reports. This is the happy consequence of technology choice made when writing the specifications of the software.

Let's go back to understand the process. When I wrote SIPINA (version 3.x), I realized that the construction of the display windows of the results took me a lot of time, rather than writing algorithms for calculations. In my view, this was not a good thing because I am away from my main concern: to understand the methods, implement, evaluate, discuss. When I thought about the specifications of Tanagra, I thought it was absolutely necessary to define a display standardized necessarily with text output, but nevertheless have a relatively attrayante. Et là, j'ai redécouvert le HTML. C'est un peu amusant à dire, surtout en 2003. Le HTML permet de faire un effort minimum de description des sorties, une seule méthode dans la classe de calcul suffit (un peu comme Weka pour ceux qui sont allés voir le code source), tout en obtenant une présentation avenante. De plus, il est possible de mettre en évidence les informations importantes à lire en priorité. Par exemple, rien que pouvoir attribuer des codes couleurs à des tranches de p-value est infiniment précieux.

Par la suite, j'ai réalisé que le choix du HTML allait s'avérer doublement judicieux. En effet, c'est un standard largement répandu. Sans effort de programmation further, we can firstly get the output into an Excel spreadsheet on the other hand, we can export the display windows in an external file and view the results in a web browser, regardless of the software Tanagra. In fact, their distribution is greatly facilitated.

These are the features of "reporting" Tanagra we present in this tutorial.

Keywords: report, reporting, decision tree, C4.5, logistic regression, coding disjunctive, ROC curve, learning sample, test sample, variable selection
Components: GROUP CHARACTERIZATION, SAMPLING, C4.5, TEST, O_1_BINARIZE, FORWARD-LOGIT, BINARY LOGISTIC REGRESSION, SCORING, ROC CURVE
Tutorial: fr_Tanagra_Reporting.pdf
Data : heart disease

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naive Bayesian continuous predictors

The classifier Naive Bayes is a supervised learning method based on a strong simplifying assumption: the descriptors (Xj) are pairwise conditionally independent values of the variable to predict (Y). Yet despite this, it proves robust and efficient. Its performance is comparable to other learning techniques. Various reasons are advanced in the literature. We ourselves proposed an explanation based on bias of representation in a previous tutorial. When predictors are discrete, one realizes easily that the naive Bayesian classifier is a linear separator. It arises in direct competition with other techniques of the same ilk, such as discriminant analysis, logistic regression, SVM (Support Vector Machine) linear, etc..

In this tutorial, we describe the model of conditional independence within the framework of quantitative predictor variables. The situation is somewhat more complex. We shall see that the simplifying assumptions used, it can be considered as a linear or quadratic separator. It is then possible to produce a classifier explicit, easy to use for deployment. The ideas put forward in this tutorial have been implemented in Tanagra 1.4.37 (and later). This representation model is original. I have not found in other free software that I used to follow (for now ...).

This paper is organized as follows. Firstly (Section 2), we detail the theoretical aspects of the method. We show that it is possible to reach an explicit model that can be expressed as a linear combination of variables or variables of the square. In Section 3, we describe the implementation of the method using the software Tanagra. We compare the results with those of other separators linear (logistic regression, linear SVM, PLS discriminant analysis, discriminant analysis of Fisher). In Section 4, we compare the implementation of technology in various software. We will mainly focus on reading the results. Finally, Section 5, we show the usefulness of the approach on very large files. We will cover the basic "mutants" comprising 16,592 observations Predictors and 5408 with a speed beyond the reach of other techniques.

Keywords: Bayesian classifier naive model of conditional independence, 5.0.10 RapidMiner, Weka 3.7.2, 2.2.2 Knime, software R package e1071, discriminant analysis, PLS discriminant analysis, PLS regression, svm linear regression
Components: NAIVE BAYES CONTINUOUS, BINARY LOGISTIC REGRESSION, SVM, C-PLS, LINEAR DISCRIMINANT ANALYSIS
Tutorial: fr_Tanagra_Naive_Bayes_Continuous_Predictors.pdf
Data : breast ; Low Birth Weight
References:
Wikipedia, "Naive Bayes classification "
Tanagra, " Naïve Bayes classifier for discrete predictors "

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Tanagra - Version 1.4.37

Continuous Naive Bayes is a supervised learning component. It implements the model of conditional independence for continuous predictors (quantitative). The main originality lies in the production of an explicit model as a linear combination of predictor variables and, possibly, their square.

functionality reporting were improved.

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New interface for RapidMiner 5.0

La société Rapid-I, à travers leur logiciel phare RapidMiner, est un acteur très dynamique du l'informatique décisionnelle. Au-delà de l'outil, elle propose des solutions et des services dans le domaine de l'analyse prédictive, data mining et du text mining. Son site web regorge d'informations (blog, tutoriels, vidéos, forum, newsletter, wiki, etc.).

La version 5.0 de RapidMiner (Community Edition - Téléchargeable gratuitement) propose une interface profondément remaniée, s'inspirant visiblement de Knime. Les ressemblances entre les deux produits sont frappantes. Je me suis dit qu'il était opportun d'étudier cela en détail, evaluating its behavior in the context of a typical analysis. We hope to implement the following process: (1) construct and display a decision tree from a set of labeled observations, (2) save the tree in a file format PMML for later deployment and (3) assess the generalization performance of the classifier through cross-validation, (4) use the model to classify a set of unlabeled observations contained in a second file, the results (and label descriptors assigned) must be recorded in a third file in CSV format. These are

is very traditional tasks of data mining. We have repeatedly described in our courseware (eg SPAD ...). More reason to check it is easy to carry them out with this new version of RapidMiner. Indeed, with the previous version, some sequences were complicated. Establish a cross-validation, for example asking an organization, albeit a very rigorous in his mind, but not very intuitive.

Tags: RapidMiner, Knime, cross-validation, decision trees, deployment
Tutorial: fr_Tanagra_RapidMiner_5.pdf
Data : adult_rapidminer.zip
References:
Rapid-I, " RapidMiner "