1 / 7

Labs: (SVM, Multi-Dimensional Scaling, Dimension Reduction), Factor Analysis, Random Forest

Labs: (SVM, Multi-Dimensional Scaling, Dimension Reduction), Factor Analysis, Random Forest. Peter Fox Data Analytics ITWS-4600/ITWS-6600/MATP-4450/CSCI-4960 Group 3 Lab 2, November 2, 2018. If you did not complete svm. group3/ lab1_svm{1,11}.R lab1_svm{12,13}.R lab1_svm_rpart1.R.

floresk
Download Presentation

Labs: (SVM, Multi-Dimensional Scaling, Dimension Reduction), Factor Analysis, Random Forest

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Labs: (SVM, Multi-Dimensional Scaling, Dimension Reduction), Factor Analysis, Random Forest Peter Fox Data Analytics ITWS-4600/ITWS-6600/MATP-4450/CSCI-4960 Group 3 Lab 2, November 2, 2018

  2. If you did not complete svm • group3/ • lab1_svm{1,11}.R • lab1_svm{12,13}.R • lab1_svm_rpart1.R

  3. And MDS, DR • lab1_mds{1,3}.R • lab1_dr{1,4}.R • http://www.statmethods.net/advstats/mds.html • http://gastonsanchez.com/blog/how-to/2013/01/23/MDS-in-R.html

  4. randomForest > library(e1071) > library(rpart) > library(mlbench) # etc. > data(kyphosis) > require(randomForest) # or library(randomForest) > fitKF <- randomForest(Kyphosis ~ Age + Number + Start, data=kyphosis) > print(fitKF) # view results > importance(fitKF) # importance of each predictor # what else can you do? data(swiss) # fertility? lab2_rf1.R data(Glass,package=“mlbench”) # Type ~ <what>? data(Titanic) # Survived ~ . Find - Mileage~Price + Country + Reliability + Type

  5. Try these • example_exploratoryFactorAnalysis.R on dataset_exploratoryFactorAnalysis.csv (on website) • http://rtutorialseries.blogspot.com/2011/10/r-tutorial-series-exploratory-factor.html (this was the example on courses in the lecture) • http://www.statmethods.net/advstats/factor.html • http://stats.stackexchange.com/questions/1576/what-are-the-differences-between-factor-analysis-and-principal-component-analysi • Do these – lab2_fa{1,2,4,5}.R

  6. Factor Analysis data(iqitems) # data(ability) ability.irt <- irt.fa(ability) ability.scores <- score.irt(ability.irt,ability) data(attitude) cor(attitude) # Compute eigenvalues and eigenvectors of the correlation matrix. pfa.eigen<-eigen(cor(attitude)) pfa.eigen$values # set a value for the number of factors (for clarity) factors<-2 # Extract and transform two components. pfa.eigen$vectors [ , 1:factors ] %*% + diag ( sqrt (pfa.eigen$values [ 1:factors ] ),factors,factors )

  7. Glass index <- 1:nrow(Glass) testindex <- sample(index, trunc(length(index)/3)) testset <- Glass[testindex,] trainset <- Glass[-testindex,] Cor(testset) Factor Analysis?

More Related