An S4 class to represent the summa output.

`predictions`

matrix of predictions

`covariance_matrix`

Square matrix of size learners containing covariance of learners

`covariance_tensor`

Cube tensor of size learners containing the covariance of three learners

`weights`

Contains weights for each method

`nsamples`

Number representing number of samples

`nmethods`

Number representing number of learners

`majority_vote`

A numeric vector of samples constructed by weighting each learner equally

`summa`

A numeric vector of samples constructed by weighting each learner by their estimated performance

`type`

A user defined character vector describing the data under analysis

`actual_performance`

A numeric vector of learnears representing actual performance of the learners, for binary data this is balanced accuracy for ranked data this is AUC

`estimated_performance`

A numeric vector of learners representing the summa estimated performance of learners

`estimated_prevelance`

A number corresponding to estimated prevelance

`sampe_rank`

A numeric vector ranking each sample by decreased confidence belonging to positive class using the summa ensemble

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