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,
  threshold = NULL,
  obj.fn = expression(sum((predicted - actual)^2)),
  objective = "min",
  extreme = FALSE,
  features.only = FALSE,
  feature.importance = TRUE,
  pred.int = NULL,
  status = TRUE,
  seed = 123L,
  dist = NULL,
  folds = 5
)

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. Columns are matched to (IVs.train) by name when the two share the same predictor names, and positionally when they share no names at all (as in cbind(test.x_1, test.x_2) against cbind(x_1, x_2)). Names that only partly overlap the training predictors are ambiguous and return an error.
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) Number of repeated holdout re-evaluations of the learner-trial feature subsets that pass the accuracy threshold.
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.
threshold: numeric [0, 1]; NULL (default) Probability supplied to LPM.VaR over the learner-trial objective distribution to determine the objective cutoff for keeping feature combinations. Defaults to 0.80 when objective = "max" and 0.20 when objective = "min". It is not a literal objective-score cutoff.
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) Sets the LPM.VaR probability to 1 (0) for maximization (minimization) objective, i.e. the most extreme learner-trial objective value becomes the cutoff. Overrides threshold.
features.only: logical; FALSE (default) Returns only the final feature loadings along with the final feature frequencies.
feature.importance: logical; TRUE (default) Draws a two-panel diagnostic: the learner-trial objective distribution with its LPM.VaR cutoff, and 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.
seed: Optional integer random seed used for reproducible resampling, fold construction, and stochastic fitting steps. If `NULL`, the current random-number-generator state is used.
dist: options:(NULL, "NNS", "L1", "L2", "FACTOR") the method of distance calculation passed to delegated NNS.reg and NNS.stack calls. dist = NULL is the default and selects the native blended NNS distance; dist = "NNS" is an explicit alias for the default.
folds: integer; 5 (default) Number of cross-validation folds passed to the final NNS.stack call.

Value

Returns a vector of fitted values for the dependent variable test set $results, prediction intervals $pred.int, the final feature loadings $feature.weights, final feature frequencies $feature.frequency, and (for classification) the class labels $class.levels. Classification results are numeric: a factor or character DV.train yields integer class codes with a base category of 1 (label recoverable as class.levels[results]), and a numeric DV.train yields its original numeric class values.

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]))

 ## Recover the labels
 a$class.levels[a$results]
 } # }