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 How to compute the cumulative distribution functions and the percent point functions of various commonly used distributions in Excel, R and Python. I use Excel (in conjunction with Tanagra or Sipina), R and Python for the practical classes of my courses about data mining and statistics at the University. Often, I ask students to perform hypothesis tests or to calculate confidence intervals, etc. We work on computers, it is obviously out of the question to use the statistical tables to obtain the quantile or p-value of the commonly used distribution functions. In this tutorial, I present the main functions for normal distribution , Student's t-distribution , chi-squared distribution and Fisher-Snedecor distribution . I realized that students sometimes find it difficult to match the reading of statistical tables with the functions they have difficulty identifying in software. It is also an opportunity for us to verify the equivalences between the functions proposed by Excel, R (sta...

It is widely agreed that R is not a fast language. Notably, because it is an interpreted language. To overcome this issue, some solutions exists which allow to compile functions written in R. The gains in computation time can be considerable. But it depends on our ability to write code that can benefit from these tools. In this tutorial, we study the efficiency of the Luke Tierney's “compiler” package which is provided in the base distribution of R. We program two standard data analysis treatments, (1) with and (2) without using loops: the scaling of variables in a data frame; the calculation of a correlation matrix by matrix product. We compare the efficiency of non-compiled and compiled versions of these functions. We observe that the gain for the compiled version is dramatic for the version with loops, but negligible for the second variant. We note also that, in the R 3.4.2 version used, it is not needed to compile explicitly the functions containing loops because it exists a JI...

Statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. In this tutorial, we will try to identify the potentialities of StatsModels by conducting a case study in multiple linear regression. We will discuss about: the estimation of model parameters using the ordinary least squares method, the implementation of some statistical tests, the checking of the model assumptions by analyzing the residuals, the detection of outliers and influential points, the analysis of multicollinearity, the calculation of the prediction interval for a new instance. Keywords : regression, statsmodels, pandas, matplotlib Tutorial : en_Tanagra_Python_StatsModels.pdf Dataset and program : en_python_statsmodels.zip References : StatsModels : Statistics in Python

The aim of text categorization is to assign documents to predefined categories as accurately as possible. We are within the supervised learning framework, with a categorical target attribute, often binary. The originality lies in the nature of the input attribute, which is a textual document. It is not possible to implement predictive methods directly, it is necessary to go through a data preparation phase. In this tutorial, we will describe a text categorization process in Python using mainly the text mining capabilities of the scikit-learn package, which will also provide data mining methods (logistics regression). We want to classify SMS as "spam" (spam, malicious) or "ham" (legitimate). We use the “SMS Spam Collection v.1” dataset. Keywords : text mining, document categorization, corpus, bag of words, f1-score, recall, precision, dimensionality reduction, variable selection, logistic regression, scikit learn, python Tutorial : Spam identification Dataset : Corpu...

This tutorial completes the course material devoted to the Support Vector Machine approach (SVM). It highlights two important dimensions of the method: the position of the support points and the definition of the decision boundaries in the representation space when we construct a linear separator; the difficulty to determine the “best” values of the parameters for a given problem. We will use R (“e1071” package) and Python (“scikit-learn” package). Keywords : svm, package e1071, logiciel R, logiciel Python, package scikit-learn, sklearn Tutorial : SVM - Support Vector Machine Dataset and programs : svm_r_python.zip References : Tanagra Tutorial, " Support Vector Machine ", May 2017. Tanagra Tutorial, " Implementing SVM on large dataset ", July 2009.

SIPINA is known for its decision tree induction algorithms. In fact, the distribution includes two other tools that are little known to the public: REGRESS, which is specialized in multiple linear regression, we described it in one of our tutorials ; and an association rules extraction tool, called simply Association Rule Software (ARS). In this tutorial, I describe the use of the ARS tool. Its interactivity with Excel spreadsheet is its main advantage. We launch the software from Excel using the “sipina.xla” add-in. We can easily retrieve the rules in the spreadsheet. Then, we can explore them (the mined rules) using the Excel data handling capabilities. The ability to filter and sort rules according to different criteria is a great help in detecting interesting rules. This is a very important aspect because the profusion of rules can quickly confuse the data miner. Keywords : ARS, association rule software, excel spreadsheet, filtering and sorting rules, interestingness measures Comp...

In this tutorial, we study the behavior of 5 linear classifiers on artificial data. Linear models are often the baseline approaches in supervised learning. Indeed, based on a simple linear combination of predictive variables, they have the advantage of simplicity: the reading of the influence of each descriptor is relatively easy (signs and values of the coefficients); learning techniques are often (not always) fast, even on very large databases. We are interested in: (1) the naive bayes classifier; (2) the linear discriminant analysis; (3) the logistic regression; (4) the perceptron (single-layer perceptron); (5) the support vector machine (linear SVM). The experiment was conducted under R. The source code accompanies this document. My idea, besides the theme of the linear classifiers that concerns us, is also to describe the different stages of the elaboration of an experiment for the comparison of learning techniques. In addition, we show also the results provided by the linear appr...