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BalancedModel

BalancedModel(; model=nothing, balancer1=balancer_model1, balancer2=balancer_model2, ...)
BalancedModel(model;  balancer1=balancer_model1, balancer2=balancer_model2, ...)

Given a classification model, and one or more balancer models that all implement the MLJModelInterface, BalancedModel allows constructing a sequential pipeline that wraps an arbitrary number of balancing models and a classifier together in a sequential pipeline.

Operation

  • During training, data is first passed to balancer1 and the result is passed to balancer2 and so on, the result from the final balancer is then passed to the classifier for training.
  • During prediction, the balancers have no effect.

Arguments

  • model::Supervised: A classification model that implements the MLJModelInterface.
  • balancer1::Static=...: The first balancer model to pass the data to. This keyword argument can have any name.
  • balancer2::Static=...: The second balancer model to pass the data to. This keyword argument can have any name.
  • and so on for an arbitrary number of balancers.

Returns

  • An instance of type ProbabilisticBalancedModel or DeterministicBalancedModel, depending on the prediction type of model.

Example

using MLJ
using Imbalance

## generate data
X, y = Imbalance.generate_imbalanced_data(1000, 5; class_probs=[0.2, 0.3, 0.5])

## prepare classification and balancing models
SMOTENC = @load SMOTENC pkg=Imbalance verbosity=0
TomekUndersampler = @load TomekUndersampler pkg=Imbalance verbosity=0
LogisticClassifier = @load LogisticClassifier pkg=MLJLinearModels verbosity=0

oversampler = SMOTENC(k=5, ratios=1.0, rng=42)
undersampler = TomekUndersampler(min_ratios=0.5, rng=42)
logistic_model = LogisticClassifier()

## wrap them in a BalancedModel
balanced_model = BalancedModel(model=logistic_model, balancer1=oversampler, balancer2=undersampler)

## now this behaves as a unified model that can be trained, validated, fine-tuned, etc.
mach = machine(balanced_model, X, y)
fit!(mach)