Sentence examples for impacts of dataset from inspiring English sources

This study clearly demonstrate the impacts of dataset selection on sensitivity of drought index computation, which has significant implications for proper usage of drought indices and related assessments, and potentially provide some valuable references for future researches on drought indices improvements.

We also explored the impact of dataset parameters on topological accuracy.

For each example, 10 randomly generated datasets were tested to reduce the impact of dataset-specific biases introduced by sampling; each dataset was analyzed using our proposed TL-BSLA and the well-known Sparse Candidate Algorithm (SCA) structure learning method [ 22] for sample sizes ranging from 50 to 500 observations.

To further test the impact of training dataset to the performance, we establish our own training dataset, which contains 260 bmp format images.

In general, ElemNet exhibits higher impact of training dataset size compared to the Random Forest models.

Impact of training dataset size on the prediction accuracy of ElemNet (DNN model) using elemental compositions only and the best conventional ML model, Random Forest, with either raw elemental compositions (RF-Comp) and physical attributes (RF-Phys).

Finally, we investigate the impact of training dataset to the performance.

We also evaluate the impact of the dataset size and the precision of the GPS coordinates on the uniqueness of the data.

We have also evaluated the impact of the dataset size and the precision of the GPS coordinates on the uniqueness of the data, and we have found that, in some datasets, coarsening the GPS precision results in a drastic reduction of the average uniqueness.

To assess the impact of the dataset size, we applied the jackknife procedure: X mutations were randomly removed from the AV set and P SH ≤ SH_r) was estimated for this smaller subset of AVs.

Importantly, besides demonstrating the impact of the dataset on the relative performance of the algorithms, the qPCR analyses supported the utility of the motif significance assessments in choosing a suitable algorithm for peak detection in cases in which the binding motif is known.