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R: Rweka package
라벨:
Informatics
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[in fedora] R-java and R-java-devel
- 1. Users who want an R install that comes with the Fedora OpenJDK preconfigured can yum install R-java.
- 2. Developers who want an R development environment that has the Fedora OpenJDK preconfigured can yum install R-java-devel.
- 3. It does not change the default behavior of the R-core, R-devel (and R) packages.
- 4. Any addon R modules that require java to be present and configured can now use BuildRequires: R-java-devel and be built for Fedora in koji.
[in R]
- install.packages(pkgs="RWeka")
- library(RWeka)
Documentation for package `RWeka' version 0.2-4
Help Pages
[in fedora] R-java and R-java-devel
AdaBoostM1 | R/Weka Meta Learners |
Apriori | R/Weka Associators |
Bagging | R/Weka Meta Learners |
Cobweb | R/Weka Clusterers |
DBScan | R/Weka Clusterers |
DecisionStump | R/Weka Classifier Trees |
FarthestFirst | R/Weka Clusterers |
fitted.Weka_classifier | Model Predictions for R/Weka Classifiers |
IBk | R/Weka Lazy Learners |
J48 | R/Weka Classifier Trees |
JRip | R/Weka Rule Learners |
LBR | R/Weka Lazy Learners |
LinearRegression | R/Weka Classifier Functions |
list_Weka_interfaces | R/Weka interfaces |
LMT | R/Weka Classifier Trees |
Logistic | R/Weka Classifier Functions |
LogitBoost | R/Weka Meta Learners |
M5P | R/Weka Classifier Trees |
M5Rules | R/Weka Rule Learners |
make_Weka_associator | R/Weka interfaces |
make_Weka_classifier | R/Weka interfaces |
make_Weka_clusterer | R/Weka interfaces |
MultiBoostAB | R/Weka Meta Learners |
OneR | R/Weka Rule Learners |
PART | R/Weka Rule Learners |
plot.Weka_tree | R/Weka Classifier Trees |
predict.Weka_classifier | Model Predictions for R/Weka Classifiers |
predict.Weka_clusterer | Class Predictions for R/Weka Clusterers |
read.arff | Read Data from ARFF Files |
SimpleKMeans | R/Weka Clusterers |
SMO | R/Weka Classifier Functions |
Stacking | R/Weka Meta Learners |
Tertius | R/Weka Associators |
WOW | Weka Option Wizard |
write.arff | Write Data into ARFF Files |
write_to_dot | Create DOT Representations |
Test Data
install.packages("caret", dependencies = TRUE)
JRip
library(caret) library(RWeka) data(iris) TrainData = iris[,1:4] TrainClasses = iris[,5] jripFit = train(TrainData, TrainClasses,method = "JRip")
J48
data(iris) m1 = J48(Species ~ ., data = iris) m1 table(iris$Species, predict(m1)) write_to_dot(m1) if(require("party", quietly = TRUE)) plot(m1) ## Using some Weka data sets ... ## J48 DF2 = read.arff(system.file("arff", "contact-lenses.arff", package = "RWeka")) m2 = J48(`contact-lenses` ~ ., data = DF2) m2 table(DF2$`contact-lenses`, predict(m2)) if(require("party", quietly = TRUE)) plot(m2) ## M5P DF3 = read.arff(system.file("arff", "cpu.arff", package = "RWeka")) m3 = M5P(class ~ ., data = DF3) m3 if(require("party", quietly = TRUE)) plot(m3) ## Logistic Model Tree. DF4 = read.arff(system.file("arff", "weather.arff", package = "RWeka")) m4 = LMT(play ~ ., data = DF4) m4 table(DF4$play, predict(m4)) ## Larger scale example. if(require("mlbench", quietly = TRUE) && require("party", quietly = TRUE)) { ## Predict diabetes status for Pima Indian women data("PimaIndiansDiabetes", package = "mlbench") ## Fit J48 tree with reduced error pruning m5 = J48(diabetes ~ ., data = PimaIndiansDiabetes, control = "-R") plot(m5) ## (Make sure that the plotting device is big enough for the tree.) }
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라벨: Informatics
Scientist. Husband. Daddy. --- TOLLE. LEGE
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