Displaying 15 results from an estimated 15 matches similar to: "mlbench dataset question"
2011 Nov 07
2
help with programming
>
>
Dear moderators,
Please help me encode the program instructed by follows.
Thank u!
Apply the methods introduced in Sections 4.2.1 and 4.2.2, say the
> rank-based variable selection and BIC criterions, to the Boston housing
> data.
>
The Boston housing data contains 506 observations, and is publicly
available in the R package mlbench (dataset “BostonHousing”).
The
2003 Jun 17
1
User-defined functions in rpart
This question concerns rpart's facility for user-defined functions that
accomplish splitting.
I was interested in modifying the code so that in each terminal node,
a linear regression is fit to the data.
It seems that from the allowable inputs in the user-defined functions,
that this may not be possible, since they have the form:
function(y, wt, parms) (in the case of the
2008 Sep 18
1
caret package: arguments passed to the classification or regression routine
Hi,
I am having problems passing arguments to method="gbm" using the train()
function.
I would like to train gbm using the laplace distribution or the quantile
distribution.
here is the code I used and the error:
gbm.test <- train(x.enet, y.matrix[,7],
method="gbm",
distribution=list(name="quantile",alpha=0.5), verbose=FALSE,
2005 Feb 25
0
Problem using stepAIC/addterm (MASS package)
Hello,
I'm currently dealing with a rather strange problem when using the
function "stepAIC" ("MASS" package). The setting is the following: From
model learning data sets ("learndata"), I want to be able to build
prediction functions (in order to save them in a file for further use).
This is done by the function "pred.function" (see below). Therein,
2012 Mar 05
1
Forward stepwise regression using lmStepAIC in Caret
I'm looking for guidance on how to implement forward stepwise regression
using lmStepAIC in Caret.
The stepwise "direction" appears to default to "backward". When I try to
use "scope" to provide a lower and upper model, Caret still seems to
default to "backward".
Any thoughts on how I can make this work?
Here is what I tried:
itemonly <-
2011 Apr 27
0
Rule-based regression models: Cubist
Cubist is a rule-based machine learning model for regression. Parts of the
Cubist model are described in:
Quinlan. Learning with continuous classes. Proceedings
of the 5th Australian Joint Conference On Artificial
Intelligence (1992) pp. 343-348
Quinlan. Combining instance-based and model-based
learning. Proceedings of the Tenth International Conference
on Machine Learning
2011 Apr 27
0
Rule-based regression models: Cubist
Cubist is a rule-based machine learning model for regression. Parts of the
Cubist model are described in:
Quinlan. Learning with continuous classes. Proceedings
of the 5th Australian Joint Conference On Artificial
Intelligence (1992) pp. 343-348
Quinlan. Combining instance-based and model-based
learning. Proceedings of the Tenth International Conference
on Machine Learning
2009 Nov 17
2
SVM Param Tuning with using SNOW package
Hello,
Is the first time I am using SNOW package and I am trying to tune the cost
parameter for a linear SVM, where the cost (variable cost1) takes 10 values
between 0.5 and 30.
I have a large dataset and a pc which is not very powerful, so I need to
tune the parameters using both CPUs of the pc.
Somehow I cannot manage to do it. It seems that both CPUs are fitting the
model for the same values
2006 Jan 19
0
Using svm.plot with mlbench.spirals.
Hi.
I'm trying to plot a pair of intertwined spirals and an svm that
separates them. I'm having some trouble. Here's what I tried.
> library(mlbench)
> library(e1071)
Loading required package: class
> raw <- mlbench.spirals(200,2)
> spiral <- data.frame(class=as.factor(raw$classes), x=raw$x[,1], y=raw$x[,2])
> m <- svm(class~., data=spiral)
> plot(m,
2009 Apr 01
0
smv() in "e1071" and the BreastCancer data from "mlbench"
R-help,
I am trying to perform a basic anlaysis of the BreastCancer data from
"mlbench" using the svm() function in "e1071". I use the following code
library("e1071")
library("mlbench")
data(BreastCancer)
BC <- subset(BreastCancer, select=-Id)
pairs(BC)
model <- svm(Class ~ ., data=BC, cross=10)
## plot(model, BC, )
tobj <- tune.svm(Class ~ .,
1999 Aug 24
1
package mlbench updated
Hi,
Evgenia and I have copied an updated version of the mlbench package to
CRAN which contains several new data sets. We have also changed some
of the variable names to avoid name conflicts.
Best,
--
-------------------------------------------------------------------
Friedrich Leisch
Institut f?r Statistik Tel: (+43 1) 58801 10715
Technische
1999 Aug 24
1
package mlbench updated
Hi,
Evgenia and I have copied an updated version of the mlbench package to
CRAN which contains several new data sets. We have also changed some
of the variable names to avoid name conflicts.
Best,
--
-------------------------------------------------------------------
Friedrich Leisch
Institut f?r Statistik Tel: (+43 1) 58801 10715
Technische
1997 Jun 09
1
R-beta: mlbench-0.1 --- machine learning benchmark problems
I've made a package from some benchmark datasets for use with R and
uploaded it to CRAN.
Here's the Index entry:
mlbench-0.1.tar.gz:
A collection of artificial and real-world machine learning
benchmark problems, including, e.g., the boston housing
data from the UCI repository.
Written/packaged by Fritz Leisch <Friedrich.Leisch at ci.tuwien.ac.at>
Original data sets from
1997 Jun 09
1
R-beta: mlbench-0.1 --- machine learning benchmark problems
I've made a package from some benchmark datasets for use with R and
uploaded it to CRAN.
Here's the Index entry:
mlbench-0.1.tar.gz:
A collection of artificial and real-world machine learning
benchmark problems, including, e.g., the boston housing
data from the UCI repository.
Written/packaged by Fritz Leisch <Friedrich.Leisch at ci.tuwien.ac.at>
Original data sets from
1997 Jun 09
1
R-beta: mlbench-0.1 --- machine learning benchmark problems
I've made a package from some benchmark datasets for use with R and
uploaded it to CRAN.
Here's the Index entry:
mlbench-0.1.tar.gz:
A collection of artificial and real-world machine learning
benchmark problems, including, e.g., the boston housing
data from the UCI repository.
Written/packaged by Fritz Leisch <Friedrich.Leisch at ci.tuwien.ac.at>
Original data sets from