Multilayered honeycombHIVE classification model for caret

A custom model definition that lets caret::train() tune, resample and evaluate honeycombHIVE classifiers with gradient-based refinement.

Usage

honeycombHIVEmodel

Format

A list with the components caret expects of a custom model.

Value

A list defining a custom caret model, for use as caret::train(method = honeycombHIVEmodel).

Details

Training runs honeycombHIVE(refine = TRUE) with the tuned parameters. Further arguments given to caret::train() are passed on to honeycombHIVE(). Progress output is off unless verbose = TRUE is passed. Prediction uses the fitted AINet model of the final layer. caret requires column names on x. Class probabilities are not implemented (prob = NULL).

Parameters

• nAntibodies: Number of initial antibodies per layer.

• beta: Clone multiplier controlling the number of clones per antibody.

• epsilon: Threshold for network suppression to remove redundant antibodies.

• layers: Number of hierarchical layers.

• refineOptimizer: Optimizer for gradient-based refinement ("sgd", "momentum", "adagrad", "adam" or "rmsprop").

• refineSteps: Number of gradient update steps in refinement.

• refineLR: Learning rate for refinement.

See also

honeycombHIVE, bHIVEmodel, train

Examples

if (requireNamespace("caret", quietly = TRUE)) {
    X <- as.matrix(iris[, 1:4])
    y <- iris$Species
    set.seed(42)
    fit <- caret::train(
        x = X, y = y, method = honeycombHIVEmodel,
        trControl = caret::trainControl(method = "cv", number = 3),
        tuneGrid = data.frame(nAntibodies = 30, beta = 5, epsilon = 0.05,
                              layers = 2, refineOptimizer = "adam",
                              refineSteps = 3, refineLR = 0.01),
        maxIter = 10
    )
    fit
    table(Predicted = predict(fit, X), Actual = y)
}
#>             Actual
#> Predicted    setosa versicolor virginica
#>   setosa         50          0         0
#>   versicolor      0         37         5
#>   virginica       0         13        45