KernelPerceptronClassifier

mutable struct KernelPerceptronClassifier <: MLJModelInterface.Probabilistic

The kernel perceptron algorithm using one-vs-one for multiclass, from the Beta Machine Learning Toolkit (BetaML).

Hyperparameters:

kernel::Function: Kernel function to employ. See ?radial_kernel or ?polynomial_kernel (once loaded the BetaML package) for details or check ?BetaML.Utils to verify if other kernels are defined (you can alsways define your own kernel) [def: radial_kernel]
epochs::Int64: Maximum number of epochs, i.e. passages trough the whole training sample [def: 100]
initial_errors::Union{Nothing, Vector{Vector{Int64}}}: Initial distribution of the number of errors errors [def: nothing, i.e. zeros]. If provided, this should be a nModels-lenght vector of nRecords integer values vectors , where nModels is computed as (n_classes * (n_classes - 1)) / 2
shuffle::Bool: Whether to randomly shuffle the data at each iteration (epoch) [def: true]
rng::Random.AbstractRNG: A Random Number Generator to be used in stochastic parts of the code [deafult: Random.GLOBAL_RNG]

Example:

julia> using MLJ

julia> X, y        = @load_iris;

julia> modelType   = @load KernelPerceptronClassifier pkg = "BetaML"
[ Info: For silent loading, specify `verbosity=0`. 
import BetaML ✔
BetaML.Perceptron.KernelPerceptronClassifier

julia> model       = modelType()
KernelPerceptronClassifier(
  kernel = BetaML.Utils.radial_kernel, 
  epochs = 100, 
  initial_errors = nothing, 
  shuffle = true, 
  rng = Random._GLOBAL_RNG())

julia> mach        = machine(model, X, y);

julia> fit!(mach);

julia> est_classes = predict(mach, X)
150-element CategoricalDistributions.UnivariateFiniteVector{Multiclass{3}, String, UInt8, Float64}:
 UnivariateFinite{Multiclass{3}}(setosa=>0.665, versicolor=>0.245, virginica=>0.09)
 UnivariateFinite{Multiclass{3}}(setosa=>0.665, versicolor=>0.245, virginica=>0.09)
 ⋮
 UnivariateFinite{Multiclass{3}}(setosa=>0.09, versicolor=>0.245, virginica=>0.665)
 UnivariateFinite{Multiclass{3}}(setosa=>0.09, versicolor=>0.665, virginica=>0.245)