Assign a probability using a simple (network) Bayesian classifier.

Uses simple Bayesian inference to return the probability or relative likelihood or a discrete label or continuous value.

bayesProbabilitySimple(
  df,
  features,
  targetCol,
  selectedFeatureNames = c(),
  retainMinValues = 1,
  doEcdf = FALSE
)

Arguments

df	data.frame
features	data.frame with bayes-features. One of the features needs to be the label-column.
targetCol	string with the name of the feature that represents the label.
selectedFeatureNames	vector default c(). Vector of strings that are the names of the features the to-predict label depends on. If an empty vector is given, then all of the features are used (except for the label). The order then depends on the features' order.
retainMinValues	integer to require a minimum amount of data points when segmenting the data feature by feature.
doEcdf	default FALSE a boolean to indicate whether to use the empirical CDF to return a probability when inferencing a continuous feature. If false, uses the empirical PDF to return the rel. likelihood.

Value

double the probability of the target-label, using the maximum a posteriori estimate.

References

Scutari M (2010). “Learning Bayesian Networks with the bnlearn R Package.” Journal of Statistical Software, 35(3), 1--22. doi: 10.18637/jss.v035.i03 .

See also

mmb::bayesInferSimple()

Examples

feat1 <- mmb::createFeatureForBayes(
  name = "Sepal.Length", value = mean(iris$Sepal.Length))
feat2 <- mmb::createFeatureForBayes(
  name = "Sepal.Width", value = mean(iris$Sepal.Width), isLabel = TRUE)

# Assign a probability to a continuous variable (also works with nominal):
mmb::bayesProbabilitySimple(df = iris, features = rbind(feat1, feat2),
  targetCol = feat2$name, retainMinValues = 5, doEcdf = TRUE)
#> [1] 0.475