bayesProbabilityNaive: Naive Bayesian inferencing for determining the probability or...

In mmb: Arbitrary Dependency Mixed Multivariate Bayesian Models

Description

A complementary implementation using methods common in mmb, such as computing factors or segmenting data. Supports Laplacian smoothing and early-stopping segmenting, as well as PDF and CDF and selecting any subset of features for dependency.

Usage

bayesProbabilityNaive(
  df,
  features,
  targetCol,
  selectedFeatureNames = c(),
  shiftAmount = 0.1,
  retainMinValues = 1,
  doEcdf = FALSE,
  useParallel = NULL
)

Arguments

df	data.frame that contains all the feature's data
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.
shiftAmount	numeric an offset value used to increase any one probability (factor) in the full built equation. In scenarios with many dependencies, it is more likely that a single conditional probability becomes zero, which would result in the entire probability being zero. Since this is often useless, the 'shiftAmount' can be added to each factor, resulting in a non-zero probability that can at least be used to order samples by likelihood. Note that, with a positive 'shiftAmount', the result of this function cannot be said to be a probability any longer, but rather results in a comparable likelihood (a 'probability score').
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. This parameter does not have any effect if all of the variables are discrete or when doing a regression. Otherwise, for each continuous variable, the probability to find a value less then or equal - given the conditions - is returned. Note that the interpretation of probability using the ECDF much deviates and must be used with care, especially since it affects each factor in Bayes equation that is continuous. This is especially true for the case where shiftAmount > 0.
useParallel	default NULL a boolean to indicate whether to use a previously registered parallel backend. If no explicit value was given, calls foreach::getDoParRegistered() to check for a parallel backend. When using parallelism, this function calculates each factor in the numerator and denominator of the final equation in parallel.

Value

numeric probability (inferring discrete labels) or relative likelihood (regression, inferring likelihood of continuous value) or most likely value given the conditional features. If using a positive shiftAmount, the result is a 'probability score'.

Author(s)

Sebastian Hönel [e-mail]

Examples

feat1 <- mmb::createFeatureForBayes(
  name = "Petal.Length", value = mean(iris$Petal.Length))
feat2 <- mmb::createFeatureForBayes(
  name = "Petal.Width", value = mean(iris$Petal.Width))
featT <- mmb::createFeatureForBayes(
  name = "Species", iris[1,]$Species, isLabel = TRUE)

# Check the probability of Species=setosa, given the other 2 features:
mmb::bayesProbabilityNaive(
  df = iris, features = rbind(feat1, feat2, featT), targetCol = "Species")

# Now check the probability of Species=versicolor:
featT$valueChar <- "versicolor"
mmb::bayesProbabilityNaive(
  df = iris, features = rbind(feat1, feat2, featT), targetCol = "Species")

mmb documentation built on Oct. 23, 2020, 5:21 p.m.