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The classical approach of comparing models by global R2/Q2 fitting performance may or may not reflect higher predictive ability, especially when dealing with different sizes of datasets, for example.

In fact, the environment-specific datasets were the datasets which compose the multi-condition (combined) datasets, for example the multi-condition (combined) '5s.20u' datasets were composed by environment-specific '5s.5u' datasets of four environments.

Furthermore, we demonstrated that there are datasets (for example, AquaticTox) where the interval of nested cross-validation errors is wide, and in which cases the user must assess the suitability of the model for the task in hand.

In addition, several statistical analytical methods have been used for analysis of metabolomic datasets; for example, hierarchical cluster analysis (HCA) [39, 78], partial least squares discriminant analysis (PLS-DA) [52, 65], and batch-learning self-organizing map (BL-SOM) [43, 44, 54].

Analysis of multiple datasets, for example combined analysis of microarray gene expression datasets addressing similar biological questions conducted at an interpretational level by meta-analysis, can enable more accurate results.

Furthermore, while some of these similarity indices are robust to incomplete sampling and low sample effort, these require a level of data complexity that is not often available in meta-analyses of multi-taxa datasets (for example, estimation of probabilities of detection requires that the community is sampled several times and is divided into multiple spatially explicit subsamples).

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They also admit there are some limitations to the dataset for example, if houseboats and a mobile home had not been included in the London figures then the proportion of affordable properties would be even lower.

There are several related works on action recognition with IXMAS dataset, for example [23 25, 43].

Multiple ways of distribution may be available for a dataset, for example when the data is available in multiple formats.

While the differences between HumanEva dataset and IXMAS dataset, for example, different ways of actors performing the same actions, might cause some false prediction.

The bit-string fingerprint descriptor values that only had one value throughout the dataset (For example, all 0 s or all 1 s) were removed.

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