| predict.CoreModel {CORElearn} | R Documentation |
Prediction using constructed model
Description
Using a previously built model and new data, predicts the class value and probabilities for classification problem and function value for regression problem.
Usage
## S3 method for class 'CoreModel'
predict(object, newdata, ..., costMatrix=NULL,
type=c("both","class","probability"))
Arguments
object |
The model structure as returned by |
newdata |
Data frame with fresh data. |
costMatrix |
Optional cost matrix can provide nonuniform costs for classification problems. |
type |
Controls what will be return value in case of classification. |
... |
Other model dependent options for prediction. See |
Details
The function uses the object structure as returned by CoreModel and
applies it on the data frame newdata. The newdata must be transformable
using the formula specified for building the model (with dependent variable removed). If the dependent
variable is present in newdata, it is ignored.
Optional cost matrix can provide nonuniform costs for classification problems. For regression
problem this parameter is ignored. The costs can be different from the ones used for building the model
in CoreModel.
Value
For regression model a vector of predicted values for given input instances. For classification
problem the parameter type controls what is returned. With default value "both"
function returns a list with two components class
and probabilities containing predicted class values and probabilities for all class values, respectively.
With type set to "class" or "probability" the function returns only the selected component
as vector or matrix.
Author(s)
Marko Robnik-Sikonja, Petr Savicky
See Also
CORElearn,
CoreModel,
modelEval,
helpCore,
paramCoreIO.
Examples
# use iris data set
# build random forests model with certain parameters
modelRF <- CoreModel(Species ~ ., iris, model="rf",
selectionEstimator="MDL",minNodeWeightRF=5,rfNoTrees=100)
print(modelRF)
# prediction with node distribution
pred <- predict(modelRF, iris, rfPredictClass=FALSE, type="both")
# print(pred)
destroyModels(modelRF) # clean up