CORElearn (version 0.9.29)

CoreModel: Build a classification or regression model

Description

Builds a classification or regression model from the data and formula with given parameters. Classification models available are

• random forests, possibly with local weighing of basic models (parallel execution on several cores),
• decision tree with constructive induction in the inner nodes and/or models in the leaves,
• kNN and weighted kNN with Gaussian kernel,
• naive Bayesian classifier.

Regression models:

• regression trees with constructive induction in the inner nodes and/or models in the leaves,
• linear models with pruning techniques,
• locally weighted regression,
• kNN and weighted kNN with Gaussian kernel.

Usage

CoreModel(formula, data,
       model=c("rf","rfNear","tree","knn","knnKernel","bayes","regTree"),
       ..., costMatrix=NULL)

Arguments

formula

Formula specifying the response and attribute variables.

data

Data frame with training data.

model

The type of model to be learned.

...

Options for building the model. See helpCore.

costMatrix

Optional cost matrix.

Value

• The created model is not returned as a structure. It is stored internally in the package memory space and only its pointer (index) is returned. The maximum number of models that can be stored simultaneously is a parameter of the initialization function initCore and defaults to 100. Models, which are not needed may be deleted in order to free the memory using function destroyModels. By referencing the returned model, any of the stored models may be used for prediction with predict.CoreModel. What the function actually returns is a list with components:
• modelID
  index of internally stored model,
• terms
  description of prediction variables and response,
• class.lev
  class values for classification problem, null for regression problem,
• model
  the type of model used, see parameter model,
• formula
  the formula parameter passed.

Details

Parameter formula is used as a mechanism to select features (attributes) and the prediction (response) variable (class). Only simple terms can be used. Interaction terms are not supported. The simplest way is to specify just the response variable using e.g. "class ~ .". See examples below.

Parameter model controls the type of the constructed model. There are several possibilities: [object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

There are many additional parameters ... available which are used by different models. Their list and description is available by calling helpCore. Evaluation of attributes is covered in function attrEval.

The optional parameter costMatrix can provide nonuniform cost matrix for classification problems. For regression problem this parameter is ignored. The format of the matrix is costMatrix(true class, predicted class). By default uniform costs are assumed, i.e., costMatrix(i, i) = 0, and costMatrix(i, j) = 1, for i not equal to j.

References

Marko Robnik-Sikonja, Igor Kononenko: Theoretical and Empirical Analysis of ReliefF and RReliefF. Machine Learning Journal, 53:23-69, 2003

Leo Breiman: Random Forests. Machine Learning Journal, 45:5-32, 2001

Marko Robnik-Sikonja: Improving Random Forests. In J.-F. Boulicaut et al.(Eds): ECML 2004, LNAI 3210, Springer, Berlin, 2004, pp. 359-370

Marko Robnik-Sikonja: CORE - a system that predicts continuous variables. Proceedings of ERK'97 , Portoroz, Slovenia, 1997

Marko Robnik-Sikonja, Igor Kononenko: Discretization of continuous attributes using ReliefF. Proceedings of ERK'95, B149-152, Ljubljana, 1995

Majority of these references are available from http://lkm.fri.uni-lj.si/rmarko/papers/

See Also

CORElearn, predict.CoreModel, modelEval, attrEval, helpCore, paramCoreIO.

Examples

# use iris data set

# build random forests model with certain parameters
modelRF <- CoreModel(Species ~ ., iris, model="rf",
              selectionEstimator="MDL",minNodeWeight=5,rfNoTrees=100)
print(modelRF)

# build decision tree with naive Bayes in the leaves
modelDT <- CoreModel(Species ~ ., iris, model="tree", modelType=4)
print(modelDT)

# build regression tree similar to CART
instReg <- regDataGen(200)
modelRT <- CoreModel(response~., instReg, model="regTree", modelTypeReg=1)
print(modelRT)

# build kNN kernel regressor by preventing tree splitting
modelKernel <- CoreModel(response~., instReg, model="regTree",
                    modelTypeReg=7, minNodeWeight=Inf)
print(modelKernel)

# A more complex example demonstrating also destroyModels() follows.
# Test accuracy of random forest predictor with 20 trees on iris data
# using 10-fold cross-validation.
ncases <- nrow(iris)
ind <- ceiling(10*(1:ncases)/ncases)
ind <- sample(ind,length(ind))
pred <- rep(NA,ncases)
fit <- NULL
for (i in unique(ind)) {
    # Delete the previous model, if there is one.
    if (!is.null(fit)) destroyModels(fit)
    fit <- CoreModel(Species ~ ., iris[ind!=i,], model="rf", rfNoTrees=20)
    pred[ind==i] <- predict.CoreModel(fit, iris[ind==i,], type="class")
}
table(pred,iris$Species)