heuristically.wordpress.com
Compare performance of machine learning classifiers in R | Heuristic Andrew
https://heuristically.wordpress.com/2009/12/23/compare-performance-machine-learning-classifiers-r
Good-enough solutions for an imperfect world. Compare performance of machine learning classifiers in R. December 23, 2009. This tutorial demonstrates to the R novice how to create five machine learning models for classification and compare the performance graphically with ROC curves in one chart. For a simpler introduction, start with Plot ROC curve and lift chart in R. Here are some exercises left for the reader:. Is the performance good for a medical diagnostic? How about for a direct mailing campaign?
pedroconcejero.wordpress.com
A ROC curves tutorial – part I – Pedro Concejero
https://pedroconcejero.wordpress.com/2016/03/07/a-roc-curves-tutorial-part-i
Data Science and some other fReak things. A ROC curves tutorial – part I. UPDATED 8th March due to mistakes in cross table tpsa cutpoint 4. I love this thing called ROC curves. For many reasons, but maybe main reason is because it is a true multidiciplinary tool which has played a key role in Psychophysics – Signal Detection Theory. Bio-medicine -screening and diagnostic through image. And in Machine Learning – evaluating classifiers. ROC and its concepts are key for screening in medicine. ROC methodolog...
rcreditscoring.com
Credit Scoring - Credit Scoring with RCredit Scoring with R
http://www.rcreditscoring.com/category/credit-scoring
Credit Scoring with R. R and Credit Scoring. Skip to primary content. Skip to secondary content. About R Credit Scoring. The 5 Cs of Credit. Category Archives: Credit Scoring. Credit Scoring is used to assess whether borrowers are likely to pay their loans back. This section contains credit scoring related articles. Reject inference, nested conditional models and joint scores. September 24, 2013. In this talk presented to the Melbourne Users of R Network (MelbURN). Population Stability Index (PSI). As a ...
rcreditscoring.com
Useful R Libraries for Credit Scoring - Credit Scoring with RCredit Scoring with R
http://www.rcreditscoring.com/useful-r-libraries-for-credit-scoring
Credit Scoring with R. R and Credit Scoring. Skip to primary content. About R Credit Scoring. The 5 Cs of Credit. Useful R Libraries for Credit Scoring. September 25, 2012. Base R will get you only so far. Here are some of the packages that I load most often for my credit scoring projects:. For splitting data into manageable pieces. Pulling data directly from databases is preferable to exporting from the database to a csv and importing to R. This entry was posted in Credit Scoring. You may use these.
rcreditscoring.com
R - Credit Scoring with RCredit Scoring with R
http://www.rcreditscoring.com/category/r
Credit Scoring with R. R and Credit Scoring. Skip to primary content. Skip to secondary content. About R Credit Scoring. The 5 Cs of Credit. R is free statistical software that is quickly becoming the standard for data analysis. Posts related to R are found in this section. Reject inference, nested conditional models and joint scores. September 24, 2013. In this talk presented to the Melbourne Users of R Network (MelbURN). Describes using R to do some bespoke reject inference modelling. June 1, 2013.
rcreditscoring.com
Credit Scoring with R - R and Credit ScoringCredit Scoring with R | R and Credit Scoring
http://www.rcreditscoring.com/author/admin
Credit Scoring with R. R and Credit Scoring. Skip to primary content. Skip to secondary content. About R Credit Scoring. The 5 Cs of Credit. Reject inference, nested conditional models and joint scores. September 24, 2013. In this talk presented to the Melbourne Users of R Network (MelbURN). Describes using R to do some bespoke reject inference modelling. Population Stability Index (PSI). September 16, 2013. The Population Stability Index. Population Stability Index formula (PSI formula). Population shif...
ornitobg.it
Atlante Ornitologico della provincia di Bergamo
http://www.ornitobg.it/spmodel.html
Nelle mappe e dei grafici vengono indicati i rilevamenti confrontati con semplici modelli a punteggio, derivati da bibliografia, che servono a verificare il grado di completamento dell'atlante. Nei modelli la potenzialità è indicata con sfumature di colore dal verde scuro (massima idoneità) a marrone-rosso (minor idoneità). La prima mappa in alto a sinistra indica i rilevamenti finora effettuati, con la simbologia indicata sotto, sovrapposti alla mappa di idoneità. Nidificazione possibile: pallino blu.
flynet.meyerp.com
Validation - flyNet
http://flynet.meyerp.com/validation
PR- and ROC-curves are a classical way of measuring the performance of a network with respect to a known set of interactions. Those curves can be computed and plotted using the free open-source R platform. And packages such as library(minet). The set of known interactions extracted from REDfly. Is available in the file redfly.data. In order to compute coregulation statistics, here are some C scripts able to deal with large networks (up to hundreds of thousand nodes). The file fly.h. Average Jaccard Index...
btibert3.github.io
Brock Tibert - Enrollment Nerdery
http://btibert3.github.io/2013/12/07/Predictive-Analytics-in-Enrollment-Management.html
A place to collect my thoughts on data analysis within Enrollment Management. Dare I call it Enrollment Science? Where you can find me. Predictive Analytics in Enrollment Management. A simple example of predictive analytics for Enrollment Managers using FREE. You will need to think about how you incorporate your new model into your current decision making processes. Why Write this Post? My goal is to try to write a post on how we can do. In Enrollment Management using. Previous Work and Discussion. In ac...
szelenin.blogspot.com
Sergey Zelenin's blog: September 2013
http://szelenin.blogspot.com/2013_09_01_archive.html
Saturday, September 28, 2013. Выжить при крушении. Улучшаем прогноз. В прошлый раз я использовал линейную регрессию для прогнозирования того, выжил ли пассажир при крушении или нет. Точность прогноза составила 0.77, что неплохо для начала. Самыми значимыми переменными (по значению P-Value) были Sex, Fare, Age. Их я и брал в для прогнозирования. Смотрим чего не хватает. Что делать, если для некоторых значимых переменных отсуствуют значения? Я же опишу как я применил его идею. Дальше определим, от каких пе...
