sutton001

A few bugs have been fixed and some new features added. The computed contributions of individuals in PCA ( PRINCIPAL COMPONENT ANALYSIS ) have been corrected. It was not valid when we work on a subsample of our data file. This error has been reported by Mr. Gilbert Laffond. The standardization of the factors after VARIMAX ( FACTOR ROTATION ) have been corrected so that their variance coincides with the sum of the squares of the correlations with the axes, and thus with the eigen value associated to the axis. This modification has been suggested by Mr. Gilbert Laffond. During the calculation of the confidence interval of the PLS regression coefficients ( PLS CONF. INTERVAL ), an error may occur when the requested number of axes was upper than the number of predictor variables. It is now corrected. This error has been reported by Mr. Alain Morineau. In some circumstances, an error may occur in FISHER FILTERING , especially when Tanagra is run under Wine for Linux. We introduce some addit...

Model deployment is one of the main objectives of the data mining process. We want to apply a model learned on a training set on unseen cases i.e. any people coming from the population. In the classification framework, the aim is to assign to the instance its class value from their description [e.g. Apply a classifier on a new dataset (Deployment) ]. In the clustering framework, we try to detect the group which is as similar as possible to the instance according their characteristics (e.g. K-Means - Classification of a new instance ). We are concerned about the regression framework here . The aim is to predict the values of the dependent variable for unseen instances (or unlabeled instances) from the observed values on the independent variables. The process is rather basic if we handle a linear regression model. We apply the computed parameters on the unseen instances. But, it becomes difficult when we want to treat more complex models such as support vector regression with nonlinear k...

Comparing the behavior of tools is always a good way to improve them. To check and validate the implementation of methods . The validation of the implemented algorithms is an essential point for data mining tools. Even if two programmers use the same references (books, articles), the programming choice can modify the behavior of the approach (behaviors according to the interpretation of the convergence conditions for instance). The analysis of the source code is possible solution. But, if it is often available for free software, this is not the case for commercial tools. Thus, the only way to check them is to compare the results provided by the tools on a benchmark dataset . If there are divergences, we must explain them by analyzing the formulas used. To improve the presentation of results . There are certain standards to observe in the production of reports, consensus initiated by reference books and / or leader tools in the field. Some ratios should be presented in a certain way. Us...

PLS regression is a regression technique usually designed to predict the values taken by a group of Y variables (target variables, dependent variables) from a set of variables X (descriptors, independent variables). Initially defined for the prediction of continuous target variable, the PLS regression can be adapted to the prediction of one discrete variable - i.e. adapted to the supervised learning framework - in different ways . The approah is called "PLS Discriminant Analysis" in this context. It incorporates the valuable qualities that we know usually into this new framework: the ability to process a representation space with very high dimensionality, a large number of noisy and / or redundant descriptors. This tutorial is the continuation of a precedent paper dedicated to the presentation of some variants of the PLS-DA . We describe the behavior of one of them (PLS-LDA - PLS Linear Discriminant Analysis) on a learning set where the number of descriptors is moderately hig...

Multicollinearity is a statistical phenomenon in which two or more predictor variables in a multiple regression model are highly correlated. In this situation the coefficient estimates may change erratically in response to small changes in the model or the data. Multicollinearity does not reduce the predictive power or reliability of the model as a whole; it only affects calculations regarding individual predictors. That is, a multiple regression model with correlated predictors can indicate how well the entire bundle of predictors predicts the outcome variable, but it may not give valid results about any individual predictor, or about which predictors are redundant with others (Wikipedia). Sometimes the signs of the coefficients are inconsistent with the domain knowledge; sometimes, explanatory variables which seems individually significant are invalidated when we add other variables. There are two steps when we want to treat this kind of problem: (1) detecting the presence of the col...