bayesProbabilityAssign {mmb}

Assign probabilities to one or more samples, given some training data.

Description

This method uses full-dependency (simple=F) Bayesian inferencing to assign a probability to the target feature in all of the samples given in dfValid. Tests each sample using @seealso mmb::bayesProbability() or @seealso mmb::bayesProbabilitySimple(). It mostly forwards the given arguments to these functions, and you will find good documentation there.

Usage

bayesProbabilityAssign(
  dfTrain,
  dfValid,
  targetCol,
  selectedFeatureNames = c(),
  shiftAmount = 0.1,
  retainMinValues = 1,
  doEcdf = FALSE,
  online = 0,
  simple = FALSE,
  naive = FALSE,
  useParallel = NULL,
  returnProbabilityTable = FALSE
)

Arguments

dfTrain

data.frame that holds the training data.

dfValid

data.frame that holds the validation samples, for each of which a probability is sought. The convention is, that if you attempt to assign a probability to a numeric value, it ought to be found in the target column of this data frame (otherwise, the target column is not required in it).

targetCol

character the name of targeted feature, i.e., the feature to assign a probability to.

selectedFeatureNames

character defaults to empty vector which defaults to using all available features. Use this to select subsets of features and to order features.

shiftAmount

numeric an offset value used to increase any one probability (factor) in the full built equation.

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.

online

default 0 integer to indicate how many rows should be used to do inferencing. If zero, then only the initially given data.frame dfTrain is used. If > 0, then each inferenced sample will be attached to it and the resulting data.frame is truncated to this number. Use an integer large enough (i.e., sum of training and validation rows) to keep all samples during inferencing. A smaller amount as, e.g., in dfTrain, will keep the amount of data restricted, discarding older rows. A larger amount than, e.g., in dfTrain is also fine; dfTrain will grow to it and then discard rows.

simple

default FALSE boolean to indicate whether or not to use simple Bayesian inferencing instead of full. This is faster but the results are less good. If true, uses mmb::bayesProbabilitySimple(). Otherwise, uses mmb::bayesProbability().

naive

default FALSE boolean to indicate whether or not to use naive Bayesian inferencing instead of full or simple.

useParallel

boolean DEFAULT NULL this is forwarded to the underlying function mmb::bayesProbability() (only in simple=FALSE mode).

returnProbabilityTable

default FALSE boolean to indicate whether to return only the probabilities for each validation sample or whether a table with a probability for each tested label should be returned. This has no effect when inferencing probabilities for numeric values, as the table then only has one column "probability". The first column of this table is always called "rowname" and corresponds to the rownames of dfValid.

Author(s)

Sebastian Hönel [e-mail]

References

Bayes T (1763). “LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, FRS communicated by Mr. Price, in a letter to John Canton, AMFR S.” Philosophical transactions of the Royal Society of London, 370–418.

Examples

w <- mmb::getWarnings()
mmb::setWarnings(FALSE)

set.seed(84735)
rn <- base::sample(rownames(iris), 150)
dfTrain <- iris[rn[1:120], ]
dfValid <- iris[rn[121:150], !(colnames(iris) %in% "Species") ]
mmb::bayesProbabilityAssign(dfTrain, dfValid, "Species")

mmb::setWarnings(w)