NNS Boost — NNS.boost • NNS

Ensemble method for classification using the NNS multivariate regression NNS.reg as the base learner instead of trees.

Usage

NNS.boost(
  IVs.train,
  DV.train,
  IVs.test = NULL,
  type = NULL,
  depth = NULL,
  learner.trials = 100,
  epochs = NULL,
  CV.size = NULL,
  balance = FALSE,
  ts.test = NULL,
  folds = 5,
  threshold = NULL,
  obj.fn = expression(sum((predicted - actual)^2)),
  objective = "min",
  extreme = FALSE,
  features.only = FALSE,
  feature.importance = TRUE,
  pred.int = NULL,
  status = TRUE
)

Arguments

IVs.train: a matrix or data frame of variables of numeric or factor data types.
DV.train: a numeric or factor vector with compatible dimensions to (IVs.train).
IVs.test: a matrix or data frame of variables of numeric or factor data types with compatible dimensions to (IVs.train). If NULL, will use (IVs.train) as default.
type: NULL (default). To perform a classification of discrete integer classes from factor target variable (DV.train) with a base category of 1, set to (type = "CLASS"), else for continuous (DV.train) set to (type = NULL).
depth: options: (integer, NULL, "max"); (depth = NULL)(default) Specifies the order parameter in the NNS.reg routine, assigning a number of splits in the regressors, analogous to tree depth.
learner.trials: integer; 100 (default) Sets the number of trials to obtain an accuracy threshold level. If the number of all possible feature combinations is less than selected value, the minimum of the two values will be used.
epochs: integer; 2*length(DV.train) (default) Total number of feature combinations to run.
CV.size: numeric [0, 1]; NULL (default) Sets the cross-validation size. Defaults to a random value between 0.2 and 0.33 for a random sampling of the training set.
balance: logical; FALSE (default) Uses both up and down sampling to balance the classes. type="CLASS" required.
ts.test: integer; NULL (default) Sets the length of the test set for time-series data; typically 2*h parameter value from NNS.ARMA or double known periods to forecast.
folds: integer; 5 (default) Sets the number of folds in the NNS.stack procedure for optimal n.best parameter.
threshold: numeric; NULL (default) Sets the obj.fn threshold to keep feature combinations.
obj.fn: expression; expression( sum((predicted - actual)^2) ) (default) Sum of squared errors is the default objective function. Any expression(...) using the specific terms predicted and actual can be used. Automatically selects an accuracy measure when (type = "CLASS").
objective: options: ("min", "max") "max" (default) Select whether to minimize or maximize the objective function obj.fn.
extreme: logical; FALSE (default) Uses the maximum (minimum) threshold obtained from the learner.trials, rather than the upper (lower) quintile level for maximization (minimization) objective.
features.only: logical; FALSE (default) Returns only the final feature loadings along with the final feature frequencies.
feature.importance: logical; TRUE (default) Plots the frequency of features used in the final estimate.
pred.int: numeric [0,1]; NULL (default) Returns the associated prediction intervals for the final estimate.
status: logical; TRUE (default) Prints status update message in console.

Value

Returns a vector of fitted values for the dependent variable test set $results, prediction intervals $pred.int, and the final feature loadings $feature.weights, along with final feature frequencies $feature.frequency.

Note

Like a logistic regression, the (type = "CLASS") setting is not necessary for target variable of two classes e.g. [0, 1]. The response variable base category should be 1 for classification problems.
Incorporate any objective function from external packages (such as Metrics::mape) via NNS.boost(..., obj.fn = expression(Metrics::mape(actual, predicted)), objective = "min")

References

Viole, F. (2016) "Classification Using NNS Clustering Analysis" doi:10.2139/ssrn.2864711

Author

Fred Viole, OVVO Financial Systems

Examples

 ## Using 'iris' dataset where test set [IVs.test] is 'iris' rows 141:150.
 if (FALSE) { # \dontrun{
 a <- NNS.boost(iris[1:140, 1:4], iris[1:140, 5],
 IVs.test = iris[141:150, 1:4],
 epochs = 100, learner.trials = 100,
 type = "CLASS", depth = NULL, balance = TRUE)

 ## Test accuracy
 mean(a$results == as.numeric(iris[141:150, 5]))
 } # }