The year 2015 ends, 2016 begins. I wish you all a very happy year 2016.
A small statistical report on the website statistics for the past year. All sites (Tanagra, course materials, e-books, tutorials) has been visited 255,386 times this year, 700 visits per day.
Since February, the 1st, 2008, the date from which I installed the Google Analytics counter, there are 1,848,033 visits (639 visits per day).
Who are you? The majority of visits come from France and Maghreb. Then there are a large part of French speaking countries, notably because some pages are exclusively in French. In terms of non-francophone countries, we observe mainly the United States, India, UK, Germany, Brazil, ...
Which pages are visited? The pages that are most successful are those that relate to documentation about the Data Science: course materials, tutorials, links to other documents available on line, etc.. This is not really surprising. I take more time myself to write booklets and tutorials, to study the behavior of different tools, of which Tanagra.
Happy New Year 2016 to all.
Ricco.
Slideshow: Website statistics for 2015
Showing posts with label Tanagra. Show all posts
Showing posts with label Tanagra. Show all posts
Monday, January 4, 2016
Thursday, January 2, 2014
Tanagra, tenth anniversary
First of all, let me introduce you to all my wishes of happiness, health and success for the year 2014 which begins.
For Tanagra, 2014 is of quite particular importance. 10 Years ago almost to the day, the first version of the software has been put on line. Designed originally as a tool for the students and researchers in the data mining domain, the project has changed a bit of nature in recent years. Today, Tanagra is an academic project which provides a point of access to the statistical and the data mining techniques. It is addressed to students, but also to the researchers of other areas (psychology, sociology, archeology, etc.). It allows, I hope, make it more attractive, more clear, the implementation of these techniques on real case studies.
This mutation was accompanied by a refocusing of my activity. The Tanagra software is still evolving (we are at version 1.4.50), new methods are added, existing components are regularly improved, but at the same time I put emphasis on the documentation in the form of books, training materials and tutorials. The underlying idea is very simple: understanding the ins and outs of the methods is the best way to learn how to use software which proposes them.
Over the past 5 years (2009/01/01 to 2013/12/31), my site gets 677 visits per day. The 10 countries that come most often are: France, Morocco, Algeria, Tunisia, United States, India, Canada, Belgium, United Kingdom and Brazil. The page of the materials for my data mining courses is the most visited (http://eric.univ-lyon2.fr/~ricco/cours/supports_data_mining.html; 99 visits per day, 6 minutes 35 seconds average time spent on the page). At the same time, I note with great satisfaction that the English pages are overall as much visited as those in French. I think that the effort to write documentation in English is fruitful.
I hope that this work will be useful for a long time, and that 2014 will be the opportunity of exchanges always so rewarding for everybody.
Ricco.
For Tanagra, 2014 is of quite particular importance. 10 Years ago almost to the day, the first version of the software has been put on line. Designed originally as a tool for the students and researchers in the data mining domain, the project has changed a bit of nature in recent years. Today, Tanagra is an academic project which provides a point of access to the statistical and the data mining techniques. It is addressed to students, but also to the researchers of other areas (psychology, sociology, archeology, etc.). It allows, I hope, make it more attractive, more clear, the implementation of these techniques on real case studies.
This mutation was accompanied by a refocusing of my activity. The Tanagra software is still evolving (we are at version 1.4.50), new methods are added, existing components are regularly improved, but at the same time I put emphasis on the documentation in the form of books, training materials and tutorials. The underlying idea is very simple: understanding the ins and outs of the methods is the best way to learn how to use software which proposes them.
Over the past 5 years (2009/01/01 to 2013/12/31), my site gets 677 visits per day. The 10 countries that come most often are: France, Morocco, Algeria, Tunisia, United States, India, Canada, Belgium, United Kingdom and Brazil. The page of the materials for my data mining courses is the most visited (http://eric.univ-lyon2.fr/~ricco/cours/supports_data_mining.html; 99 visits per day, 6 minutes 35 seconds average time spent on the page). At the same time, I note with great satisfaction that the English pages are overall as much visited as those in French. I think that the effort to write documentation in English is fruitful.
I hope that this work will be useful for a long time, and that 2014 will be the opportunity of exchanges always so rewarding for everybody.
Ricco.
Sunday, September 15, 2013
Tanagra - Version 1.4.49
Some enhancements regarding factor analysis approaches (PCA - principal component analysis, MCA - multiple correspondence analysis, CA - correspondence analysis, FDMA - factorial analysis of mixed data) have been incorporated. In particular, outputs have been completed.
The VARIMAX rotation has been improved. Thanks to Frédéric Glausinger for optimized source code.
The Benzecri correction is added in the MCA outputs. Thanks to Bernard Choffat for this suggestion.
Download page : setup
The VARIMAX rotation has been improved. Thanks to Frédéric Glausinger for optimized source code.
The Benzecri correction is added in the MCA outputs. Thanks to Bernard Choffat for this suggestion.
Download page : setup
Saturday, December 1, 2012
Tanagra - Version 1.4.48
New components have been added.
K-Means Strengthening. This component was suggested to me by Mrs. Claire Gauzente. The idea is to strengthen an existing partition (e.g. from a HAC) by using K-Means algorithm. A comparison of groups before and after optimization is proposed, indicating the efficiency of the optimization. The approach can be plugged to all clustering algorithm into Tanagra. Thanks to Claire for this valuable idea.
Discriminant Correspondence Analysis. This is an extension of the canonical discriminant analysis to discrete attributes (Hervé Abdi, 2007). The approach is based on a clever transformation of the dataset. The initial dataset is transformed into a crosstab. The values of the target attribute are in row, all the values of the input attributes are in column. The algorithm performs a correspondence analysis to this new data table to identify the associations between the values of the target and the input variables. Thus, we dispose of the tools of the correspondence analysis for a comprehensive reading of the results (factor scores, contributions, quality of representation).
Other components have been improved.
HAC. After the choice of the number of groups in the dendrogram in the Hierarchical Agglomerative Clustering, a last pass on the data is performed, it assigns each individual of the learning sample into the group with the nearest centroid. Thus, there may be discrepancy between the number of instances displayed on the tree nodes and the number of individuals in the groups. Tanagra displays the two partitions. Only the last one is used when Tanagra applies the clustering model on new instances, when it computes conditional statistics, etc.
Correspondence Analysis. Tanagra now provides the coefficients of the factor score functions for supplementary columns and rows in the factorial correspondence analysis. Thus, it will be possible to easily calculate the factor scores of new points described by their row or column profile. Finally, the results tables can be sorted according to contributions to the factors of the modalities.
Multiple correspondence analysis. Several improvements have been made to the multiple correspondence analysis: the component knows how to take into account supplementary continuous and discrete variables; the variables can be sorted according to their contribution to the factors; all indicators for the interpretation can be brought together in a single large table for a synthetic visualization of the results, this feature is especially interesting if we have a small number of factors; the coefficients for the factor score functions are provided, we can easily calculate the factorial coordinates of the supplementary individuals apart from Tanagra.
Some tutorials will come soon to describe the use of these components on realistic case studies.
Download page : setup
K-Means Strengthening. This component was suggested to me by Mrs. Claire Gauzente. The idea is to strengthen an existing partition (e.g. from a HAC) by using K-Means algorithm. A comparison of groups before and after optimization is proposed, indicating the efficiency of the optimization. The approach can be plugged to all clustering algorithm into Tanagra. Thanks to Claire for this valuable idea.
Discriminant Correspondence Analysis. This is an extension of the canonical discriminant analysis to discrete attributes (Hervé Abdi, 2007). The approach is based on a clever transformation of the dataset. The initial dataset is transformed into a crosstab. The values of the target attribute are in row, all the values of the input attributes are in column. The algorithm performs a correspondence analysis to this new data table to identify the associations between the values of the target and the input variables. Thus, we dispose of the tools of the correspondence analysis for a comprehensive reading of the results (factor scores, contributions, quality of representation).
Other components have been improved.
HAC. After the choice of the number of groups in the dendrogram in the Hierarchical Agglomerative Clustering, a last pass on the data is performed, it assigns each individual of the learning sample into the group with the nearest centroid. Thus, there may be discrepancy between the number of instances displayed on the tree nodes and the number of individuals in the groups. Tanagra displays the two partitions. Only the last one is used when Tanagra applies the clustering model on new instances, when it computes conditional statistics, etc.
Correspondence Analysis. Tanagra now provides the coefficients of the factor score functions for supplementary columns and rows in the factorial correspondence analysis. Thus, it will be possible to easily calculate the factor scores of new points described by their row or column profile. Finally, the results tables can be sorted according to contributions to the factors of the modalities.
Multiple correspondence analysis. Several improvements have been made to the multiple correspondence analysis: the component knows how to take into account supplementary continuous and discrete variables; the variables can be sorted according to their contribution to the factors; all indicators for the interpretation can be brought together in a single large table for a synthetic visualization of the results, this feature is especially interesting if we have a small number of factors; the coefficients for the factor score functions are provided, we can easily calculate the factorial coordinates of the supplementary individuals apart from Tanagra.
Some tutorials will come soon to describe the use of these components on realistic case studies.
Download page : setup
Thursday, March 29, 2012
Tanagra - Version 1.4.43
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 additional checking. This error has been reported by Mr. Bastien Barchiési.
The checking of missing values is now optional. The performance can be preferred for the treatment of very large files. We find the performances of 1.4.41 and previous versions.
The "COMPONENT / COPY RESULTS" menu sends information in HTML format. It is now compatible with the spreadsheet Calc of Libre Office 3.5.1. It was operating with the Excel spreadsheet only before. Curiously, the copy to the OOCalc (Open Office spreadsheet) is not possible at the present time (Open Office 3.3.0).
Donwload page : setup
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 additional checking. This error has been reported by Mr. Bastien Barchiési.
The checking of missing values is now optional. The performance can be preferred for the treatment of very large files. We find the performances of 1.4.41 and previous versions.
The "COMPONENT / COPY RESULTS" menu sends information in HTML format. It is now compatible with the spreadsheet Calc of Libre Office 3.5.1. It was operating with the Excel spreadsheet only before. Curiously, the copy to the OOCalc (Open Office spreadsheet) is not possible at the present time (Open Office 3.3.0).
Donwload page : setup
Saturday, February 4, 2012
Tanagra - Version 1.4.42
The Tanagra.xla add-in for Excel can work now for both the 32 and 64-bit versions of EXCEL.
With the FastMM memory manager, Tanagra can address up to 3 GB under 32-bit Windows and 4 GB under 64-bit Windows. The processing capabilities, especially about the handling of large datasets, are improved.
The importation of the tab-delimited text file format and xls file format (Excel 97-2003) is made safer. Previously, the importation is interrupted and the dataset is truncated when an invalid line is read (with missing or inconsistent values). Now, Tanagra skips the line and continues on the next rows. The number of skipped lines is reported into the importation report.
Donwload page : setup
With the FastMM memory manager, Tanagra can address up to 3 GB under 32-bit Windows and 4 GB under 64-bit Windows. The processing capabilities, especially about the handling of large datasets, are improved.
The importation of the tab-delimited text file format and xls file format (Excel 97-2003) is made safer. Previously, the importation is interrupted and the dataset is truncated when an invalid line is read (with missing or inconsistent values). Now, Tanagra skips the line and continues on the next rows. The number of skipped lines is reported into the importation report.
Donwload page : setup
Monday, January 2, 2012
Tanagra website statistics for 2011
The year 2011 ends, 2012 begins. I wish you all a very happy year 2012.
A small statistical report on the website statistics for the past year. All sites (Tanagra, course materials, e-books, tutorials) has been visited 281,352 times this year, 770 visits per day. For comparison, we had 662 daily visits in 2010, 520 in 2009, 349 in 2008.
Who are you? The majority of visits come from France and Maghreb. Then there are a large part of French speaking countries. In terms of non-francophone countries, we observe mainly the United States, India, UK, Italy, Brazil, Germany,...
Which pages are visited? The pages that are most successful are those that relate to documentation about the Data Mining: course materials, tutorials, links to other documents available on line, etc.. This is not really surprising. I take more time myself to write booklets and tutorials, to study the behavior of different software, of which Tanagra.
Happy New Year 2012 to all.
Ricco.
Slideshow: Website statistics for 2011
A small statistical report on the website statistics for the past year. All sites (Tanagra, course materials, e-books, tutorials) has been visited 281,352 times this year, 770 visits per day. For comparison, we had 662 daily visits in 2010, 520 in 2009, 349 in 2008.
Who are you? The majority of visits come from France and Maghreb. Then there are a large part of French speaking countries. In terms of non-francophone countries, we observe mainly the United States, India, UK, Italy, Brazil, Germany,...
Which pages are visited? The pages that are most successful are those that relate to documentation about the Data Mining: course materials, tutorials, links to other documents available on line, etc.. This is not really surprising. I take more time myself to write booklets and tutorials, to study the behavior of different software, of which Tanagra.
Happy New Year 2012 to all.
Ricco.
Slideshow: Website statistics for 2011
Thursday, September 22, 2011
Tanagra - Version 1.4.41
A PRIORI PT. This component generates association rules. It is based on the Borgelt's apriori.exe program which has been recently updated (2011/09/02 - 5.57 version). The improvement of this new version, in terms of calculation time, is impressive.
FREQUENT ITEMSETS. Also based on the Borgelt's apriori.exe program (version 5.57), this component generates frequent (or closed, maximum, generators) itemsets.
Some tutorials are coming soon to describe the use of these new tools.
Donwload page : setup
FREQUENT ITEMSETS. Also based on the Borgelt's apriori.exe program (version 5.57), this component generates frequent (or closed, maximum, generators) itemsets.
Some tutorials are coming soon to describe the use of these new tools.
Donwload page : setup
Tuesday, July 5, 2011
Tanagra - Version 1.4.40
Few improvements for this new version.
A new addon for the connection between Tanagra and the recent version of OpenOffice Calc spreadsheet has been created. The old one did not work for recent versions - OpenOffice 3.3 and LibreOffice 3.4. During the installation process, another library was added ("TanagraModule.oxt") to not interfere with the old, still functional for previous versions of Open Office (3.2 and earlier). A tutorial describing its installation and its utilization will be put online soon. I take this opportunity to highlight again how a privileged connection between a spreadsheet and a specialized tool for Data Mining is convenient. The annual poll organized by the kdnuggets.com website shows the interest of this connection (2011, 2010, 2009,...). We note that there is a similar addon for the R software (R4Calc). This change was suggested by Jérémy Roos (OpenOffice) and Franck Thomas (LibreOffice).
The non-standardized ACP is now available. It is possible to implement unchecking the option of standardization of the data in the Principal Component Analysis component. Change suggested by Elvire Antanjan.
Simultaneous regression was introduced. It is very similar to the method programmed into LazStats, which is unfortunately more accessible freely now. The approach is described in a free booklet online "Practice of linear regression analysis" (in French) (section 3.6).
The color codes according to the p-value have been introduced for the Linear Correlation component. Change suggested by Samuel KL.
Once again, thank you very much to all those who help me to improve this work by their comments or suggestions.
Donwload page : setup
A new addon for the connection between Tanagra and the recent version of OpenOffice Calc spreadsheet has been created. The old one did not work for recent versions - OpenOffice 3.3 and LibreOffice 3.4. During the installation process, another library was added ("TanagraModule.oxt") to not interfere with the old, still functional for previous versions of Open Office (3.2 and earlier). A tutorial describing its installation and its utilization will be put online soon. I take this opportunity to highlight again how a privileged connection between a spreadsheet and a specialized tool for Data Mining is convenient. The annual poll organized by the kdnuggets.com website shows the interest of this connection (2011, 2010, 2009,...). We note that there is a similar addon for the R software (R4Calc). This change was suggested by Jérémy Roos (OpenOffice) and Franck Thomas (LibreOffice).
The non-standardized ACP is now available. It is possible to implement unchecking the option of standardization of the data in the Principal Component Analysis component. Change suggested by Elvire Antanjan.
Simultaneous regression was introduced. It is very similar to the method programmed into LazStats, which is unfortunately more accessible freely now. The approach is described in a free booklet online "Practice of linear regression analysis" (in French) (section 3.6).
The color codes according to the p-value have been introduced for the Linear Correlation component. Change suggested by Samuel KL.
Once again, thank you very much to all those who help me to improve this work by their comments or suggestions.
Donwload page : setup
Thursday, May 26, 2011
Tanagra - Version 1.4.39
Some minor corrections for the Tanagra 1.4.39 version.
For the PCA (principal component analysis) component, when we ask all the factors, none are generated. Reported by Jérémy Roos.
In the previous 1.4.38 version, the results of Multinomial Logistic Regression are not consistent with the tutorial on the website. The calculations are wrong. Reported by Nicole Jurado.
It is now possible to obtain the scores from the PLS-DA component (Partial Least Squares Regression - Discriminant Analysis). Reported by Carlos Serrano.
All these bugs are corrected in the 1.4.39 version. Once again, thank you very much to all those who help me to improve this work by their comments or suggestions.
Donwload page : setup
For the PCA (principal component analysis) component, when we ask all the factors, none are generated. Reported by Jérémy Roos.
In the previous 1.4.38 version, the results of Multinomial Logistic Regression are not consistent with the tutorial on the website. The calculations are wrong. Reported by Nicole Jurado.
It is now possible to obtain the scores from the PLS-DA component (Partial Least Squares Regression - Discriminant Analysis). Reported by Carlos Serrano.
All these bugs are corrected in the 1.4.39 version. Once again, thank you very much to all those who help me to improve this work by their comments or suggestions.
Donwload page : setup
Friday, February 4, 2011
Tanagra - Version 1.4.38
Some minor corrections for the Tanagra 1.4.38 version.
The color codes for the normality tests have been harmonized (Normality Test). In some configurations, the colors associated with p-values were not consistent, it could misleading the users. This problem has been reported by Lawrence M. Garmendia.
Following indications from Mr. Oanh Chau, I realized that the standardization of variables to the HAC (hierarchical agglomerative clustering) was based on the sample standard deviation. This is not an error in itself. But the sum of index of level into the dendrogram does not consistent with the TSS (total sum of squares). This is unwelcome. The difference is especially noticeable on small dataset, it disappears when the dataset size increases. The correction has been introduced. Now the BSS ratio is equal to 1 when we have the trivial partition i.e. one individual per group.
Multiple linear regression (MULTIPLE LINEAR REGRESSION) displays the matrix (X'X) ^ (-1). It allows to deduce the variance covariance matrix of coefficients (by multiplying the matrix by the estimated variance of the error). It can be also used in the generalized tests for the model coefficients.
Last, the outputs of the descriptive discriminant analysis (CANONICAL DISCRIMINANT ANALYSIS) were improved. The group centroids (Group centroids) on the factorial axes are directly provided.
Thank you very much to all those who help me to improve this work by their comments or suggestions.
Download page: setup
The color codes for the normality tests have been harmonized (Normality Test). In some configurations, the colors associated with p-values were not consistent, it could misleading the users. This problem has been reported by Lawrence M. Garmendia.
Following indications from Mr. Oanh Chau, I realized that the standardization of variables to the HAC (hierarchical agglomerative clustering) was based on the sample standard deviation. This is not an error in itself. But the sum of index of level into the dendrogram does not consistent with the TSS (total sum of squares). This is unwelcome. The difference is especially noticeable on small dataset, it disappears when the dataset size increases. The correction has been introduced. Now the BSS ratio is equal to 1 when we have the trivial partition i.e. one individual per group.
Multiple linear regression (MULTIPLE LINEAR REGRESSION) displays the matrix (X'X) ^ (-1). It allows to deduce the variance covariance matrix of coefficients (by multiplying the matrix by the estimated variance of the error). It can be also used in the generalized tests for the model coefficients.
Last, the outputs of the descriptive discriminant analysis (CANONICAL DISCRIMINANT ANALYSIS) were improved. The group centroids (Group centroids) on the factorial axes are directly provided.
Thank you very much to all those who help me to improve this work by their comments or suggestions.
Download page: setup
Tuesday, January 4, 2011
Tanagra website statistics for 2010
The year 2010 ends, 2011 begins. I wish you all a very happy year 2011.
A small statistical report on the website statistics for the past year. All sites (Tanagra, course materials, e-books, tutorials) has been visited 241,765 times this year, 662 visits per day. For comparison, we had 520 daily visits in 2009 and 349 in 2008.
Who are you? The majority of visits come from France and Maghreb (62%). Then there are a large part of French speaking countries. In terms of non-francophone countries, we observe mainly the United States, India, UK, Germany, Brazil,...
Which pages are visited? The pages that are most successful are those that relate to documentation about the Data Mining: course materials, tutorials, links to other documents available on line, etc.. This is hardly surprising. I take more time myself to write booklets and tutorials, to study the behavior of different software, of which Tanagra.
Happy New Year 2011 to all.
Ricco.
Slideshow: Website statistics for 2010
A small statistical report on the website statistics for the past year. All sites (Tanagra, course materials, e-books, tutorials) has been visited 241,765 times this year, 662 visits per day. For comparison, we had 520 daily visits in 2009 and 349 in 2008.
Who are you? The majority of visits come from France and Maghreb (62%). Then there are a large part of French speaking countries. In terms of non-francophone countries, we observe mainly the United States, India, UK, Germany, Brazil,...
Which pages are visited? The pages that are most successful are those that relate to documentation about the Data Mining: course materials, tutorials, links to other documents available on line, etc.. This is hardly surprising. I take more time myself to write booklets and tutorials, to study the behavior of different software, of which Tanagra.
Happy New Year 2011 to all.
Ricco.
Slideshow: Website statistics for 2010
Tuesday, October 19, 2010
Tanagra - Version 1.4.37
Naive Bayes Continuous is a supervised learning component. It implements the naive bayes principle for continuous predictors (gaussian assumption, heteroscedasticity or homoscedasticity). The main originality is that it provides an explicit model corresponding to a linear combination of predictors and, eventually, their square.
Enhancement of the reporting module.
Enhancement of the reporting module.
Tuesday, March 23, 2010
Tanagra - Version 1.4.36
ReliefF is a component for automatic variable selection in a supervised learning task. It can handle both continuous and discrete descriptors. It can be inserted before any supervised method.
Naive Bayes was modified. It now described a prediction model in an explicit form (in a linear combination form), easy to understand and to deploy.
Naive Bayes was modified. It now described a prediction model in an explicit form (in a linear combination form), easy to understand and to deploy.
Tuesday, January 19, 2010
Tanagra - Version 1.4.35
CTP. The method of detection of the right size of the tree is modified for the "Clustering Tree" with post-pruning component (CTP). It relies both on the angle between half-lines at each point on the curve of decreasing the WSS (within-group sum of squares) on the growing sample and the decrease of the same indicator computed on the pruning sample. Compared to the previous implementation, it results in a smaller number of clusters.
Regression Tree. The previous modification is incorporated into the Regression Tree component which is a univariate version of CTP.
C-RT Regression Tree. A new regression tree component was added. It faithfully implements the technique described in the Breiman's and al. (1984) book, including the post-pruning part with the 1-SE Rule (Chapter 8, especially p. 226 about the formula for the variance of the MSE).
C-RT. The report of the induction of decision tree C-RT has been completed. Based on the last column of the post-pruning table, it becomes easier to choose the parameter x (in x-SE Rule) to arbitrarily define the size of the pruned tree.
Some tutorials will describe these various changes soon.
Sunday, November 22, 2009
Tanagra - Version 1.4.34
A component of induction of predictive rules (RULE INDUCTION) was added under "Supervised Learning" tab. Its use is described in a tutorial available online (will be translated soon).
The DECISION LIST component has been improved, we changed the test done during the pre-pruning process. The formula is described in the tutorial above.
The SAMPLING and STRATIFIED SAMPLING components (Instance Selection tab) have been slightly modified. It is now possible to set ourself the seed number of the pseudorandom number generator.
Following an indication of Anne Viallefont, calculation of degrees of freedom in tests on contingency tables is now more generic. Indeed, the calculation was wrong when the database was filtered and some margins (row or column) contained a number equal to zero. Anne, thank you for this information. More generally, thank you to everyone who sent me comments. Programming has always been for me a kind of leisure. The real work starts when it is necessary to check the results, compare them with the available references, cross them with other data mining tools, free or not, understand the possible differences, etc.. At this step, your help is really valuable.
The DECISION LIST component has been improved, we changed the test done during the pre-pruning process. The formula is described in the tutorial above.
The SAMPLING and STRATIFIED SAMPLING components (Instance Selection tab) have been slightly modified. It is now possible to set ourself the seed number of the pseudorandom number generator.
Following an indication of Anne Viallefont, calculation of degrees of freedom in tests on contingency tables is now more generic. Indeed, the calculation was wrong when the database was filtered and some margins (row or column) contained a number equal to zero. Anne, thank you for this information. More generally, thank you to everyone who sent me comments. Programming has always been for me a kind of leisure. The real work starts when it is necessary to check the results, compare them with the available references, cross them with other data mining tools, free or not, understand the possible differences, etc.. At this step, your help is really valuable.
Saturday, October 3, 2009
Tanagra - Version 1.4.33
Several logistic regression diagnostics and evaluation tools were implemented, one of them (reliability diagram) can be applied to any supervised method
1.The estimated covariance matrix
2. Hosmer - Lemeshow Test
3. Reliability diagram (says also calibration plot)
4. Analysis of residuals, outilers and influentials points (pearson residuals, deviance residuals, dfichisq, difdev, levier, Cook's distance, dfbeta, dfbetas)
A tutorial describing the utilization of these tools will be available soon.
1.The estimated covariance matrix
2. Hosmer - Lemeshow Test
3. Reliability diagram (says also calibration plot)
4. Analysis of residuals, outilers and influentials points (pearson residuals, deviance residuals, dfichisq, difdev, levier, Cook's distance, dfbeta, dfbetas)
A tutorial describing the utilization of these tools will be available soon.
Monday, September 28, 2009
Using batch mode for Tanagra
For large simulations, it is more convenient to use BATCH mode capabilities of Tanagra rather than opening interactive session. This is the case for instance when we compare the performance of various algorithms on the same dataset; when we try to find automatically the best parameters for a learning method; when we repeat the same treatment on different datasets, etc. In these contexts, it is more useful to save the diagrams in text mode (.TDM file format). It will be easier to handle it outside TANAGRA, with a text editor for instance.
In this tutorial, we want to compare the performances of the naïve bayes classifier with and without the feature selection process. We know that the naïve bayes classifier is highly sensitive to irrelevant features. The goal of this tutorial is to evaluate the efficiency of the FCBF feature selection method in this context.
Keywords: batch mode, supervised learning, naive bayes, feature selection, experiments
Components: NAIVE BAYES, FCBF, CROSS VALIDATION
Tutorial: english_dr_utiliser_tanagra_en_mode_batch.pdf
Dataset: tanagra_batch_execution.zip
In this tutorial, we want to compare the performances of the naïve bayes classifier with and without the feature selection process. We know that the naïve bayes classifier is highly sensitive to irrelevant features. The goal of this tutorial is to evaluate the efficiency of the FCBF feature selection method in this context.
Keywords: batch mode, supervised learning, naive bayes, feature selection, experiments
Components: NAIVE BAYES, FCBF, CROSS VALIDATION
Tutorial: english_dr_utiliser_tanagra_en_mode_batch.pdf
Dataset: tanagra_batch_execution.zip
Wednesday, April 15, 2009
Tanagra - Version 1.4.31
Thierry Leiber has improved the add-on making the connection between Tanagra and Open Office. It is now possible, under Linux, to install the add-on for Open Office and launch Tanagra directly after selecting the data (see the tutorials on installing Tanagra under Linux and the integration of add-on in Open Office Calc). Thierry, thank you very much for this contribution which helps the users of Tanagra.
Following a suggestion of Mr. Laurent Bougrain, the confusion matrix is added to the automatic saving of results in experiments. Thank you to Laurent, and all others, who by their constructive comments helps me upgrade Tanagra in the right direction.
In addition, two new components for regression using the support vector machine principle (support vector regression) were added: Epsilon-Nu-SVR and SVR. A tutorial shows these methods and compare our results with the R software will be available soon. Tanagra, as with the R package "e1071", are based on the famous LIBSVM library.
Tutorials about these releases are coming soon.
Following a suggestion of Mr. Laurent Bougrain, the confusion matrix is added to the automatic saving of results in experiments. Thank you to Laurent, and all others, who by their constructive comments helps me upgrade Tanagra in the right direction.
In addition, two new components for regression using the support vector machine principle (support vector regression) were added: Epsilon-Nu-SVR and SVR. A tutorial shows these methods and compare our results with the R software will be available soon. Tanagra, as with the R package "e1071", are based on the famous LIBSVM library.
Tutorials about these releases are coming soon.
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