CORElearn (version 1.48.0)

modelEval: Statistical evaluation of predictions

Description

Using predictions of given model produced by predict.CoreModel and correct labels, computes some statistics evaluating the quality of the model.

Usage

modelEval(model=NULL, correctClass, predictedClass, predictedProb=NULL,  costMatrix=NULL, priorClProb = NULL,  avgTrainPrediction = NULL, beta = 1)

Arguments

model

The model structure as returned by CoreModel, or NULL if some other predictions are evaluated.

correctClass

A vector of correct class labels for classification problem and function values for regression problem.

predictedClass

A vector of predicted class labels for classification problem and function values for regression problem.

predictedProb

An optional matrix of predicted class probabilities for classification.

costMatrix

Optional cost matrix can provide nonuniform costs for classification problems.

priorClProb

If model=NULL a vector of prior class probabilities shall be provided in case of classification.

avgTrainPrediction

If model=NULL mean of prediction values on training set shall be provided in case of regression.

beta

For two class problems beta controls the relative importance of precision and recall in F-measure.

Value

For classification problem function returns list with the componentsFor regression problem the returned list has components

Details

The function uses the model structure as returned by CoreModel, predictedClass and predictedProb returned by predict.CoreModel. Predicted values are compared with true values and some statistics are computed measuring the quality of predictions. In classification only one of the predictedClass and predictedProb can be NULL (one of them is computed from the other under assumption that class label is assigned to the most probable class). Some of the returned statistics are defined only for two class problems, for which the confusion matrix specifying the number of instances of true/predicted class is defined as follows,

true/predicted class	positive
negative	positive
true positive (TP)	false negative (FN)

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 and prediction with the model in predict.CoreModel. If no costs are supplied, uniform costs are assumed. The format of the matrix is costMatrix(true_class, predicted_class). By default a uniform costs are assumed, i.e., costMatrix(i, i) = 0, and costMatrix(i, j) = 1, for i not equal to j. See the example below. If a non-CORElearn model is evaluated, one should set model=NULL, and a vector of prior of class probabilities priorClProb shall be provided in case of classification, and in case of regression avgTrainPrediction shall be the mean of prediction values (estimated on a e.g., training set).

References

Igor Kononenko, Matjaz Kukar: Machine Learning and Data Mining: Introduction to Principles and Algorithms. Horwood, 2007

David J.Hand: Good practice in retail credit scorecard assesment. Journal of Operational Research Society, 56:1109-1117, 2005)

See Also

CORElearn, CoreModel, predict.CoreModel.

Examples

# use iris data

# build random forests model with certain parameters
model <- CoreModel(Species ~ ., iris, model="rf", 
              selectionEstimator="MDL",minNodeWeightRF=5,
              rfNoTrees=100, maxThreads=1)

# prediction with node distribution
pred <- predict(model, iris, rfPredictClass=FALSE)

# Model evaluation
mEval <- modelEval(model, iris[["Species"]], pred$class, pred$prob)
print(mEval)

# use nonuniform cost matrix
noClasses <- length(levels(iris[["Species"]]))
costMatrix <- 1 - diag(noClasses)
costMatrix[3,1] <- costMatrix[3,2] <- 5 # assume class 3 is more valuable  
mEvalCost <- modelEval(model, iris[["Species"]], pred$class, pred$prob, costMatrix=costMatrix)
print(mEvalCost)

destroyModels(model) # clean up