sutton001

The statistical approach of the "text mining" consists in to transform a collection of text documents in a matrix of numeric values on which we can apply machine learning algorithms. The "unstructured document" designation is often used when one talks about text documents. This does not mean that he does not have a certain organization (titles, chapters, paragraphs, questions and answers, etc.). It shows first of all that we cannot express directly the collection in the form of a data table that is usually handled in data mining. To obtain this kind of data representation, a preprocessing phase is needed, then we extract relevant features to define the data table. These steps can influence heavily the relevance of the results. In this tutorial, I take an exercise that I lead with my students for my text mining course at the University. We perform all the analysis under R with the dedicated packages for text mining such as “XML” or “tm”. The issue here is to perform ...

The aim of image mining is to extract valuable knowledge from image data. In the context of supervised image classification, we want to assign automatically a label to image from their visual content. The whole process is identical to the standard data mining process. We learn a classifier from a set of classified images. Then, we can apply the classifier to a new image in order to predict its class membership. The particularity is that we must extract a vector of numerical features from the image before to launch the machine learning algorithm, and before to apply the classifier in the deployment phase. We deal with an image classification task in this tutorial. The goal is to detect automatically the images which contain a car. The main result is that, even if I have a basic knowledge about the image processing, I can lead the analysis with a facility which is symptomatic of the usability of Knime in this context. Keywords : image mining, image classification, image processing, featu...

The "gradient boosting" is an ensemble method that generalizes boosting by providing the opportunity of use other loss functions ("standard" boosting uses implicitly an exponential loss function). These slides show the ins and outs of the method. Gradient boosting for regression is detailed initially. The classification problem is presented thereafter. The solutions implemented in the packages for R and Python are studied. Keywords : boosting, regression tree, package gbm, package mboost, package xgboost, R, Python, package scikit-learn, sklearn Slides : Gradient Boosting References : R. Rakotomalala, " Bagging, Random Forest, Boosting ", December 2015. Natekin A., Knoll A., " Gradient boosting machines, a tutorial ", in Frontiers in Neurorobotics , December 2013. 

The add-ins “tangra.xla” and “sipina.xla” are greatly involved in popularity of Tanagra and Sipina software applications. They incorporate menus dedicated to data mining in Excel. They implement a simple bridge between the data into the spreadsheet and Tanagra or Sipina. I developed and tested the latest add-ins versions for Excel 2007 and 2010. I had access recently to Excel 2016. I checked the add-ins. The conclusion is that the tools work without a hitch. Keywords : data importation, excel data file, add-in, add-on, xls, xlsx Lien : en_Tanagra_Add_In_Excel_2016.pdf References : Tanagra, " Tanagra add-in for Excel 2007 and 2010 ", August 2010. Tanagra, " Sipina add-in for Excel 2007 and 2010 ", June 2016.

SIPINA is a Data Mining Software which implements various supervised learning paradigms. This is an old tool but it is still used because this is the only free tool which provides fully functional interactive decision tree capabilities. This tutorial briefly describes the installation and the use of the add-in "sipina.xla" into Excel 2007. The approach is easily generalized to Excel 2010. A similar document exists for Tanagra . It seemed to me nevertheless necessary to clarify the procedure, especially because several users have made the request. Other tutorials exist for earlier versions of Excel (1997-2003)  and for Calc (Libre Office and Open Office). A new tutorial will come soon. It shows that the add-in operates properly also under Excel 2016 . Keywords : data importation, excel data file, add-in, add-on, xls, xlsx Tutorial : en_sipina_excel_addin.pdf Dataset : heart.xls References : Tanagra, " Tanagra add-in for Office 2007 and Office 2010 ", august 2010. Tan...

The aim of the logistic regression is to build a model for predicting a binary target attribute from a set of explanatory variables (predictors, independent variables), which are numeric or categorical. They are treated as such when they are numeric. We must recode them when they are categorical. The dummy coding is undeniably the most popular approach in this context. The situation becomes more complicated when we perform a feature selection . The idea is to determine the predictors that contribute significantly to the explanation of the target attribute. There is no problem when we consider a numeric variable. It is either excluded or either kept in the model. But how to proceed when we handle a categorical explanatory variable? Should we treat the dichotomous variables associated to a categorical predictor as a whole that we must exclude or include into the model? Or should we treat the each dichotomous variable independently? How to interpret the coefficients of the selected dichot...

In this tutorial, we show how to perform a dummy coding for categorical predictor variables in the context of the logistic regression learning process. In fact, this is an old tutorial that I was written a long time ago (2007), but it is not referenced in this blog (which was created in 2008). I found it in my archives because I plan to write soon a tutorial about the strategies for the selection of categorical variables in logistic regression. I was wondering if I had already written something that may be linked to this subject (the treatment of the categorical predictors in logistic regression) in the past. Obviously, I would have to check most often my archives. We use Tanagra 1.4.50 in this tutorial. Keywords : logistic regression, dummy coding, categorical predictor variables Components : SAMPLING, O_1_BINARIZE, BINARY LOGISTIC REGRESSION, TEST Tutorial : Dummy coding - Logistic Regression Dataset : heart-c.xlsx   References : Wikipedia, " Logistic Regression "