Predictor Rules

NBClassifier

Structure(Classifier) NBClassifier(Structure(DataGridStats) dataGridStats1, ...)

Builds a Naive Bayes Classifier structure from a set of data grids stats that encode the target conditional probabilities. Each data grid stats results from a preparation model (data grid) and input values, which allows the computation of conditional probabilities.

SNBClassifier

Structure(Classifier) SNBClassifier(
  Structure(Vector) variableWeights, Structure(DataGridStats) dataGridStats1, ...
)

Builds a Selective Naive Bayes Classifier structure. The first parameter is weight Vector for the selected variables. The remaining parameters are the same as for the NBClassifier rule.

TargetValue

Categorical TargetValue(Structure(Classifier) classifier)

Computes a Classifier's most probable target value.

TargetProb

Numerical TargetProb(Structure(Classifier) classifier)

Computes the Classifiers probability of the most probable target value.

TargetProbAt

Numerical TargetProbAt(Structure(Classifier) classifier, Categorical targetValue)

Computes the Classifier probability (score) of a given target value.

BiasedTargetValue

Categorical BiasedTargetValue(Structure(Classifier) classifier, Structure(Vector) biasValues)

Computes the Classifier highest score target value, after adding a bias to each initial target value score.

NBRankRegressor

Structure(RankRegressor) NBRankRegressor(Structure(DataGridStats) dataGridStats1, ...)

Builds a Naive Bayes RankRegressor structure from a set of data grids stats.

SNBRankRegressor

Structure(RankRegressor) SNBRankRegressor(
  Structure(Vector) variableWeights, Structure(DataGridStats) dataGridStats1, ...
)

Builds a Selective Naive Bayes RankRegressor. The first parameter is weight Vector for the selected variables. The remaining parameters are the same as for the NBRankRegressor rule.

TargetRankMean

Numerical TargetRankMean(Structure(RankRegressor) rankRegressor)

Computes the RankRegressor target rank mean.

TargetRankStandardDeviation

Numerical TargetRankStandardDeviation(Structure(RankRegressor) rankRegressor)

Computes the RankRegressor target rank's standard deviation.

TargetRankDensityAt

Numerical TargetRankDensityAt(Structure(RankRegressor) rankRegressor, Numerical rank)

Computes the RankRegressor density of the target rank for a given normalized rank (between 0 and 1).

TargetRankCumulativeProbAt

Numerical TargetRankCumulativeProbAt(Structure(RankRegressor) rankRegressor, Numerical rank)

Computes the RankRegressor probability that the target rank is below a given normalized rank.

NBRegressor

Structure(Regressor) NBRegressor(
  Structure(RankRegressor) nbRankRegressor, Structure(DataGrid) targetValues
)

Builds a Naive Bayes Regressor structure. The first parameter is a Naive Bayes RankRegressor. The second parameter is the distribution of the numerical target values, encoded as a univariate numerical data grid based on a vector of values partition.

SNBRegressor

Structure(Regressor) SNBRegressor(
  Structure(RankRegressor) snbRankRegressor, Structure(DataGrid) targetValues
)

Builds a Selective Naive Bayes Regressor structure from a RankRegressor.The first parameter is a Naive Bayes RankRegressor. The second parameter is the distribution of the numerical target values, encoded as a univariate numerical data grid based on a vector of values partition.

TargetMean

Numerical TargetMean(Structure(Regressor) regressor)

Computes the Regressor mean target value.

TargetStandardDeviation

Numerical TargetStandardDeviation(Structure(Regressor) regressor)

Computes the Regressor standard deviation of the target value.

TargetDensityAt

Numerical TargetDensityAt(Structure(Regressor) regressor, Numerical value)

Computes the Regressor density of the target for a given value.