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It is noteworthy to mention that this ensemble was obtained by combining several hundreds of poor and highly predictive models instead of only the most predictive models of each class, namely GBM, RF and SVM (Table 2D and Figure 4).

The output of the NN ensemble was obtained using Equation (1) and the MCC was calculated based on it.

A mathematical model of all signals present in the spectral ensemble was obtained by spectral deconvolution using fast maximum likelihood reconstruction (see FMLR section of methods for details).

Each ensemble is obtained by varying the features used to train their matchers.

The final prediction of the ensemble is obtained by voting using the optimal weight vector of classifiers.

The random samples in the posterior parameter ensemble are obtained by using measurements to update 50 prior parameter samples generated with a Latin Hypercube Sampling (LHS) procedure.

The similarity of any clusters in the cluster ensemble is obtained from a weighted graph of clusters.

The final ensemble is obtained by subsequent optimization using PHENIX and filtering based on fit-to-data, measured using a cross-validation parameter R free; lower R free implies better fit-to-data.

The configurational canonical partition function associated with such an ensemble is obtained by marginalising the momenta in (11): (21) Z N, V, T ≡ ∫ d r N exp ⁡ − β U r N. It may also be defined as (22) Q N, V, T ≡ M N Z N, V, T, where the constant (23) M N = 1 h 2 β / 2 π m A 3 N A N A ! h 2 β / 2 π m B 3 N B N B ! ⋯ takes into account the indiscernibility of the constituent atoms.

The displacement ensembles are obtained by processing six strain measurements.

Several candidate ensembles are obtained through extracting base classifiers with better performance iteratively and incrementally.