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The naive bayes approach is a supervised learning method which is based on a simplistic hypothesis: it assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature. Yet, despite this, it appears robust and efficient. Its performance is comparable to other supervised learning techniques. We introduce in Tanagra ( version 1.4.36 and later) a new presentation of the results of the learning process. The classifier is easier to understand, and its deployment is also made easier. In the first part of this tutorial, we present some theoretical aspects of the naive bayes classifier. Then, we implement the approach on a dataset with Tanagra. We compare the obtained results (the parameters of the model) to those obtained with other linear approaches such as the logistic regression, the linear discriminant analysis and the linear SVM. We note that the results are highly consistent. This largely explains the good perform...

In this tutorial, we will be interested in SPAD. This is a French software specialized in exploratory data analysis which evolved much these last years. We would perform a sequence of analysis from a dataset collected into 3 worksheets of a Excel data file: (1) we create a classification tree from the learning sample into the first worksheet, we try to analyze deeply some nodes of the tree to highlight the characteristics of covered instances, we try also to modify interactively (manually) the properties of some splitting operation; (2) we apply the classifier on unseen cases of the second worksheet; (3) we compare the prediction of the model with the actual values of the target attribute contained into the third worksheet. Of course, we can perform this process using free tools such as SIPINA (the interactive construction of the tree) or R (the programming of the sequence of operations, in particular the applying of the model on unlabeled dataset). But with Spad or other commercial to...

In real problems, the classes are not equally represented in dataset. The instances corresponding to positive class, the one that we want to detect often, are few. For instance, in a fraud detection problem, there are a very few cases of fraud comparing to the large number of honest connections; in a medical problem, the ill persons are fortunately rare; etc. In these situations, using the standard learning process and assessing the classifier with the confusion matrix and the misclassification rate are not appropriate. We observe that the default classifier consisting to assign all the instances to the majority class is the one which minimizes the error rate. For the dataset that we analyze in this tutorial, 1.77% of all the examples belong to the positive class. If we assign all the instances to the negative class - this is the default classifier - the misclassification rate is 1.77%. It is difficult to find a classifier which is able to do better. Even if we know that we have not a ...

The processing of very large datasets is a crucial problem in data mining. To handle them, we must avoid to load the whole dataset into memory. The idea is quite simple: (1) we write all or a part of the dataset on the disk in a binary file format to allow a direct access; (2) the machine learning algorithms must be modified to efficiently access the values stored on the disk. Thus, the characteristics of the computer are no longer a bottleneck for the handling of a large dataset. In this tutorial, we describe the great "filehash" package for R. It allows to copy (to dump) any kind of R objects into a file. We can handle these objects without loading them into main memory. This is especially useful for the data frame object. Indeed, we can perform a statistical analysis with the usual functions directly from a database on the disk. The processing capacities are vastly improved and, in the same time, we will note that the increase in computation time remains moderate. To evalu...

This tutorial describes the implementation of tools for the diagnostic and the assessment of a logistic regression. These tools are available in Tanagra version 1.4.33 (and later). We deal with a credit scoring problem. We try to determine by using logistic regression the factors underlying the agreement or refusal of a credit to customers. We perform the following steps: - Estimating the parameters of the classifier; - Retrieving the covariance matrix of coefficients; - Assessment using the Hosmer and Lemeshow goodness of fit test; - Assessment using the reliability diagram; - Assessment using the ROC curve; - Analysis of residuals, detection of outliers and influential points. On the one hand, we use Tanagra 1.4.33 . Then, on the other hand, we perform the same analysis using the R 2.9.2 software [glm(.) procedure] . Keywords : logistic regression, residual analysis, outliers, influential points, pearson residual, deviance residual, leverage, cook's distance, dfbeta, dfbetas, hos...

The discretization transforms a continuous attribute into a discrete one. To do that, it partitions the range into a set of intervals by defining a set of cut points. Thus we must answer to two questions to lead this data transformation: (1) how to determine the right number of intervals; (2) how to compute the cut points. The resolution is not necessarily in that sequence. The best discretization is the one performed by an expert domain. Indeed, he takes into account other information than those only provided by the available dataset. Unfortunately, this kind of approach is not always feasible because: often, the domain knowledge is not available or it does not allow to determine the appropriate discretization; the process cannot be automated to handle a large number of attributes. So, we are often forced to found the determination of the best discretization on a numerical process. Discretization of continuous features as preprocessing for supervised learning process. First, we must d...

SIPINA is a data mining tool. But it is also a machine learning method. It corresponds to an algorithm for the induction of decision graphs (see References, section 9). A decision graph is a generalization of a decision tree where we can merge any two terminal nodes of the graph, and not only the leaves issued from the same node. The SIPINA method is only available under the version 2.5 of SIPINA data mining tool . This version has some drawbacks. Among others, it cannot handle large datasets (higher than 16.383 instances). But it is the only tool which implements the decision graphs algorithm. This is the main reason for which this version is available online to date. If we want to implement a decision tree algorithm such as C4.5 or CHAID, or if we want to create interactively a decision tree , it is more advantageous to use the research version (named also version 3.0). The research version is more powerful and it supplies much functionality for the data exploration. In this tutoria...