Ensembles

Bagging

SDeMo.Bagging Type

Bagging

source

SDeMo.outofbag Function

outofbag(ensemble::Bagging; kwargs...)

This method returns the confusion matrix associated to the out of bag error, wherein the succes in predicting instance i is calculated on the basis of all models that have not been trained on i. The consensus of the different models is a simple majority rule.

The additional keywords arguments are passed to predict.

source

SDeMo.bootstrap Function

bootstrap(y, X; n = 50)

Generate a series of n bootstrap samples for molde bagging. The present and absent classes are boostrapped separately so that in and out of bag respect (on average) class balance.

source

bootstrap(sdm::SDM; kwargs...)

source

SDeMo.iqr Function

iqr(x, m=0.25, M=0.75)

Returns the inter-quantile range, by default between 25% and 75% of observations.

source

Heterogeneous ensembles

SDeMo.Ensemble Type

Ensemble

An heterogeneous ensemble model is defined as a vector of SDMs. Bagging models can also be used.

source