similar to: good boosting tutorial and package in R?

Hi all, Could anybody please recommend some hands-on books on classification, data-mining and machine learning with R? I would like to get a very good understanding of the statistical tools that are used in these areas, while reducing the learning curve. Thank you!

I'm interested in further understanding the differences in using many classification trees to improve classification rates. I'm also interested in finding out what I can do in R and which methods will allow prediction. Can anybody point me to a citation or discussion? Specifically, I want to classify remotely sensed imagery where training data is extracted on class membership by the user.

Hi I am using boosting for a classification and prediction problem. For some reason it is giving me an outcome that doesn't fall between 0 and 1 for the predictions. I have tried type="response" but it made no difference. Can anyone see what I am doing wrong? Screen output shown below: > boost.model <- gbm(as.factor(train$simNuance) ~ ., # formula +

Hi, We''ve just migrated one of our rails applications from nginx/passenger running on REE 1.8.7 Ubuntu 8.04 to unicorn running on MRI 1.9.3 on Ubuntu 10.04. The app makes a number of calls to internal services using curb. Our deployment script stops unicorn by sending SIGQUIT to the unicorn master, sleeps for a few seconds to ensure that HAProxy has taken the node out of service and

Hi List, I can't seem to find a package that implements boosting for multi-class classification. Does such a package exist?

Greetings, I have been exploring the use of the caret package to conduct some plsda modeling. Previously, I have come across methods that result in a R2 and Q2 for the model. Using the 'iris' data set, I wanted to see if I could accomplish this with the caret package. I use the following code: library(caret) data(iris) #needed to convert to numeric in order to do regression #I

Hi, and sorry for asking such an unspecific question. Does anybody know of statistical / data mining methods that are available in R that are not in SAS ? With SAS I mean the SAS System Version 9.4 and SAS Enterprise Miner. I don't expect a complete list, just two or three examples or hints where and what to look for. I found some older comparisons, and the R methods mentioned there

Hi all, Could anybody give me some pointers to Cross Validation using Lifting Score as error function, as commonly used in data-mining and classification field in marketing and e-commerce research? Thanks!

Hi all, I am doing binary classification and want to improve the classification results on imbalanced response data. Currently the performance is poor. Are there ways I could improve the performance? I could either try different classification methodologies, or try exploring the data more, and throwing away noisy data, and manipulate the data more before sending into the classifiers. I was

I''m trying to use Feedzirra within my rails app and am having a mountain of trouble trying to get the gem installed. Running "gem install pauldix-feedzirra" gives me the following output. ERROR: Error installing pauldix-feedzirra: ERROR: Failed to build gem native extension. "C:/Program Files/Ruby/186-26/bin/ruby.exe" extconf.rb checking for curl-config...

Dear R users, I’m trying to understand how correlated predictors impact the Relative Importance measure in Stochastic Boosting Trees (J. Friedman). As Friedman described “ …with single decision trees (referring to Brieman’s CART algorithm), the relative importance measure is augmented by a strategy involving surrogate splits intended to uncover the masking of influential variables by others

Hi all, For some odd reason when running na?ve bayes, k-NN, etc., I get slightly different results (e.g., error rates, classification probabilities) from run to run even though I am using the same random seed. Nothing else (input-wise) is changing, but my results are somewhat different from run to run. The only randomness should be in the partitioning, and I have set the seed before this

Hello, does any one know how to interpret this output in R? > Classification with logitboost > fit <- logitboost(xlearn, ylearn, xtest, presel=50, mfinal=20) > summarize(fit, ytest) Minimal mcr: 0 achieved after 6 boosting step(s) Fixed mcr: 0 achieved after 20 boosting step(s) What is "mcr" mean? Thanks [[alternative HTML version deleted]]

Hi, I am trying to implement the Adaboost.M1. algorithm as described in "The Elements of Statistical Learning" p.301 I don't use Dtettling 's library "boost" because : - I don't understande the difference beetween Logitboost and L2boost - I 'd like to use larger trees than stumps. By using option weights set to (1/n, 1/n, ..., 1/n) in rpart or tree

> On Aug 14, 2017, at 12:22 PM, fs <mail at friedrich-schuster.de> wrote: > > Hi, and sorry for asking such an unspecific question. > > Does anybody know of statistical / data mining methods that are available in R > that are not in SAS ? With SAS I mean the SAS System Version 9.4 and SAS > Enterprise Miner. I don't expect a complete list, just two or three

In case you don't get an answer from someone more knowledgeable: 1. I don't know. 2. But it is possible that other packages that are loaded after set.seed() fool with the RNG. 3. So I would call set.seed just before you invoke each random number generation to be safe. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking

Hi, Dear R community, Does anyone know how to constructdecision tree with boosting? Is any tutorial I can read? -- Sincerely, Changbin -- [[alternative HTML version deleted]]

On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> wrote: (Sorry to be a bit slow responding.) You have not supplied a complete example, which would be good in this case because what you are suggesting could be a serious bug in R or a package. Serious journals require reproducibility these days. For example, JSS is very clear on this point. To your question >

Hi UseRs - I am new to R, and could use some help making out-of-sample predictions using a boosting model (the mboost command). The issue is complicated by the fact that I have panel data (time by country), and am estimating the model separately for each country. FYI, this is monthly data and I have 1986m1 - 2009m12 for 9 countries. To give you a flavor of what I am doing, here is a simple

I'm using caret to assess classifier performance (and it's great!). However, I've found that my results differ between R2.* and R3.* - reported accuracies are reduced dramatically. I suspect that a code change to kernlab ksvm may be responsible (see version 5.16-24 here: http://cran.r-project.org/web/packages/caret/news.html). I get very different results between caret_5.15-61 +