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The predictive value of those animal studies is carefully monitored, thus providing an ideal dataset for evaluating the efficacy of animal models.
Furthermore, we state the argument that the dataset for evaluating the video quality monitoring model should be more challenging than that for video system planning model.
We used this information (i.e., edge file) as the test dataset for evaluating the algorithm, and constructed communities using the infomap community-detection algorithm of the IGraph package.
In this article, we first differentiate the requirements for VQA model from the two application scenarios, and state the argument that the dataset for evaluating the quality monitoring model should be more challenging than that for system planning model.
Correspondingly, the dataset for evaluating the model performance should be more challenging than that for planning model, i.e., the proposed model should work well not only on dataset-1 but also on dataset-2.
The dataset is useful for building QSAR models for predicting activity against one or more specific targets for novel compounds and will also serve as a benchmark dataset for evaluating the performance of various machine-learning algorithms, especially multi-target learning algorithms.
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Split the dataset into training and test datasets for evaluating the predicted performance of the model.
1. Split the dataset into training and test datasets for evaluating the predicted performance of the model.
The first attempt to characterize datasets for evaluating the performance of learning algorithms was made by Rendell et al. [24].
High-resolution carbon maps produced by ALS provide powerful datasets for evaluating the environmental drivers of forest structure, such as wind.
This strategy provided a grand total of 336 composite datasets for evaluating the performance of the different model selection strategies.
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