Example Use of 'mlpack' from C++ via R

Description

A Minimal Example Package which demonstrates 'mlpack' use via C++ Code from R.

Package Content

Index of help topics:

covertype_small         Covertype data subset used for classification
kMeans                  Run a k-means clustering analysis
linearRegression        Run a linear regression with optional ridge
                        regression
loanData                Loan data subset used for default prediction
loanDefaultPrediction   loanDefaultPrediction
randomForest            Run a Random Forest classificatio
rcppmlpackexamples-package
                        Example Use of 'mlpack' from C++ via R

Maintainer

Dirk Eddelbuettel <edd@debian.org>

Author(s)

Dirk Eddelbuettel [aut, cre], Authors of mlpack [aut], Constantinos Giachalis [ctb]

Covertype data subset used for classification

Description

A subset of the UCI machine learning data set ‘covertype’ describing cloud coverage in seven different states of coverage. This smaller subset contains with 100,000 observations and 55 variables. The first 54 variables are explanatory (i.e. “features”), with the last providing the dependent variable (“labels”. The data is in the ‘wide’ 55 x 100,000 format used by mlpack. The dependent variable has been transformed to the range zero to six by subtracting one from the values found in the data file.

Details

The original source of the data is the US Forest Service, and the complete file is part of the UC Irvince machine learning data repository.

Source

https://www.mlpack.org/datasets/covertype-small.csv.gz

References

https://archive.ics.uci.edu/dataset/31/covertype

Run a k-means clustering analysis

Description

Run a k-means clustering analysis, returning a list of cluster assignments

Usage

kMeans(data, clusters)

Arguments

Details

This function performs a k-means clustering analysis on the given data set.

Value

A list with cluster assignments

Examples

x <- rbind(matrix(rnorm(100, sd = 0.3), ncol = 2),
           matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2))
colnames(x) <- c("x", "y")
cl <- kMeans(x, 2)

data(trees, package="datasets")
cl2 <- kMeans(t(trees),3)

Run a linear regression with optional ridge regression

Description

Run a linear regression (with optional ridge regression)

Usage

linearRegression(matX, vecY, lambda = 0, intercept = TRUE)

Arguments

Details

This function performs a linear regression, and serves as a simple test case for accessing an MLPACK function.

Value

A vector with fitted values

Examples

suppressMessages(library(utils))
data("trees", package="datasets")
X <- with(trees, cbind(log(Girth), log(Height)))
y <- with(trees, log(Volume))
lmfit <- lm(y ~ X)
# summary(fitted(lmfit))
mlfit <- linearRegression(X, y)
# summary(mlfit)
all.equal(unname(fitted(lmfit)),  as.vector(mlfit))

Loan data subset used for default prediction

Description

A four column data set containing a binary variable ‘Employed’ (with zero denoting unemployment and one employment), a numeric variable ‘Bank Balance’, a numeric variable ‘Annual Salary’ and a binary target variable ‘Defaulted?’ (with zero denoting loan repayment and one denoting default).

Details

The original source of the data is not documented by mlpack.

Source

https://datasets.mlpack.org/LoanDefault.csv

References

https://archive.ics.uci.edu/dataset/31/covertype

loanDefaultPrediction

Description

Predict loan default using a decision tree model

Usage

loanDefaultPrediction(loanDataFeatures, loanDataTargets, pct = 0.25)

Arguments

Details

This functions performs a loan default prediction based on three variables on employment, bank balance and annual salary to predict loan repayment or default

Value

A list object with predictions, probabilities, accuracy and a report matrix

Examples

data(loanData)
res <- loanDefaultPrediction(t(as.matrix(loanData[,-4])),  # col 1 to 3, transposed
                             loanData[, 4],                # col 4 is the target
                             0.25)                         # retain 25% for testing
str(res)
res$report

Run a Random Forest classificatio

Description

Run a Random Forest Classifier

Usage

randomForest(dataset, labels, pct = 0.3, nclasses = 7L, ntrees = 10L)

Arguments

Details

This function performs a Random Forest classification on a subset of the standard ‘covertype’ data set

Value

A list object

See Also

covertype_small

Examples

data(covertype_small)                         # see help(covertype_small)
res <- randomForest(covertype_small[-55,],    # features (already transposed)
                    covertype_small[55,],     # labels now in [0, 6] range
                    0.3)                      # percentage used for testing
str(res)  # accuracy varies as method is randomized but not seed set here