CORElearn (version 0.9.29)

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, 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 shell be provided in case of classification.

avgTrainPrediction

If model=NULL mean of prediction values on training set shell 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 components
• accuracy
  classification accuracy
• averageCost
  average classification cost
• informationScore
  statistics measuring information in the predicted probabilities
• AUC
  Area under the ROC curve
• predictionMatrix
  matrix of miss-classifications
• sensitivity
  sensitivity for two class problems
• specificity
  specificity for two class problems
• brierScore
  Brier score of predicted probabilities
• kappa
  Kappa statistics measuring randomness of the predictions
• precision
  precision for two class problems
• recall
  recall for two class problems (the same as sensitivity)
• F-measure
  F-measure giving a weighted score of precision and recall for two class problems
• G-mean
  geometric mean of positive and negative accuracy
• For regression problem the returned list has components
• MSE
  Mean Squared Error
• RMSE
  Relative Mean Squared Error
• MAE
  Mean Absolute Error
• RMAE
  Relative Mean Absolute Error

Details

The function uses the model structure as returned by CoreModel, predictedClass and (optionally) predictedProb returned by predict.CoreModel. Predicted values are compared with true values and some statistics are computed measuring the quality of predictions. 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 predicting with the model in predict.CoreModel. If no costs are supplied uniform costs are assumed where necessary. If a non CORElearn model is evaluated, one should set model=NULL, and a vector of prior of class probabilities priorClProb shell be provided in case of classification, and in case of regression avgTrainPrediction shell be mean of prediction values on training set.

References

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

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",minNodeWeight=5,rfNoTrees=100)

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

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