mlearning (version 1.2.1)

prior: Get or set priors on a confusion matrix

Description

Most metrics in supervised classifications are sensitive to the relative proportion of the items in the different classes. When a confusion matrix is calculated on a test set, it uses the proportions observed on that test set. If they are representative of the proportions in the population, metrics are not biased. When it is not the case, priors of a confusion object can be adjusted to better reflect proportions that are supposed to be observed in the different classes in order to get more accurate metrics.

Usage

prior(object, ...)
# S3 method for confusion
prior(object, ...)
prior(object, ...) <- value
# S3 method for confusion
prior(object, ...) <- value

Value

prior() returns the current class frequencies associated with the first classification tabulated in the confusion object, i.e., for rows in the confusion matrix.

Arguments

object

a confusion object (or another class if a method is implemented)

...

further arguments passed to methods

value

a (named) vector of positive numbers of zeros of the same length as the number of classes in the confusion object. It can also be a single >= 0 number and in this case, equal probabilities are applied to all the classes (use 1 for relative frequencies and 100 for relative frequencies in percent). If the value has zero length or is NULL, original prior probabilities (from the test set) are used. If the vector is named, names must correspond to existing class names in the confusion object.

See Also

confusion()

Examples

data("Glass", package = "mlbench")
# Use a little bit more informative labels for Type
Glass$Type <- as.factor(paste("Glass", Glass$Type))
# Use learning vector quantization to classify the glass types
# (using default parameters)
summary(glass_lvq <- ml_lvq(Type ~ ., data = Glass))

# Calculate cross-validated confusion matrix
(glass_conf <- confusion(cvpredict(glass_lvq), Glass$Type))

# When the probabilities in each class do not match the proportions in the
# training set, all these calculations are useless. Having an idea of
# the real proportions (so-called, priors), one should first reweight the
# confusion matrix before calculating statistics, for instance:
prior1 <- c(10, 10, 10, 100, 100, 100) # Glass types 1-3 are rare
prior(glass_conf) <- prior1
glass_conf
summary(glass_conf, type = c("Fscore", "Recall", "Precision"))

# This is very different than if glass types 1-3 are abundants!
prior2 <- c(100, 100, 100, 10, 10, 10) # Glass types 1-3 are abundants
prior(glass_conf) <- prior2
glass_conf
summary(glass_conf, type = c("Fscore", "Recall", "Precision"))

# Weight can also be used to construct a matrix of relative frequencies
# In this case, all rows sum to one
prior(glass_conf) <- 1
print(glass_conf, digits = 2)
# However, it is easier to work with relative frequencies in percent
# and one gets a more compact presentation
prior(glass_conf) <- 100
glass_conf

# To reset row class frequencies to original propotions, just assign NULL
prior(glass_conf) <- NULL
glass_conf
prior(glass_conf)