bayesProbabilitySimple: Assign a probability using a simple (network) Bayesian...
In mmb: Arbitrary Dependency Mixed Multivariate Bayesian Models

Description Usage Arguments Value Author(s) References See Also Examples

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
)

`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.

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

Sebastian Hönel sebastian.honel@lnu.se

\insertRef

rpkg:bnlearn_4.5mmb

mmb::bayesInferSimple()

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)