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B. The model ensemble is then applied on the test set.

Therefore, the combination of these models in a model ensemble is expected to lead to higher predictive power than individual models ("wisdom of crowds").

The model with the lowest AIC value of the model ensemble is the best.

Statistics are computed from the measures found in the null ensemble (and the direction in which that nestedness measure is calculated), before the next null model ensemble is instantiated.

In summary, it is well understood that model design is an iterative procedure, and while our logical analysis of numerical results suggested the step-wise addition or elimination of new features, there is no mathematical proof that the model ensemble is truly unique.

The two methods for model ensemble were the Granger-Ramanathan (G-R) and the Bates-Granger (B-G).

The final model ensemble was used to predict the activities on the test set, and the error in prediction compared to that of single PCM models on the same set.

The model ensemble was identified using 14 different steady-state and dynamic data sets taken from literature.

A multi-model ensemble is a good strategy to evaluate the uncertainty, and much valuable knowledge has been obtained from ensemble experiments (e.g., van der Linden and Mitchell 2009; Means et al. 2013; Evans et al. 2014).

Model ensembles were created with the help of the R package caretEnsemble [50].

Both single PCM models and model ensembles were used to predict the bioactivities for the test set, and their error in prediction compared.