Sentence examples for dataset performance from inspiring English sources

One of the most important parameters in the performance evaluation of a model is training dataset performance.

In addition, by comparing the training and testing dataset performance, it appears that the ANFIS-PSO is totally trapped in over-fitting and the results of this method cannot be used in practical situations.

Therefore, the comparison between the best ANFIS-GA and ANFIS-DE models signifies that despite the models' close performance in the testing dataset, the training dataset performance of the ANFIS-GA with RMSE of 0.040 is most likely trapped in over-fitting, and the ANFIS-DE result with RMSE of 0.057 in the training dataset is more reliable.

Table 7 Performance on the ELEA dataset   ELEA dataset performance   Extraversion Leadership Kindiroglu et al. [2] 72.5 – Aran and Gatica-Perez [38] 74.5 – Okada et al. [1] 69.6 74.5 Baseline ridge 75.5 68.6 Feature selection and RF 76.5 72.5 MTL LASSO 76.5 70.6 Transfer lasso and dirty 78.4 – Forward selection L21 81.3 74.5.

We found that overall, human dataset performance is strongly predicted by node degree (correlation of 0.94) with a variable fraction (approximately half to two-thirds) of the performance accountable by node degree performance (including in the meta-analysis dataset and protein-interaction dataset) (Figure 5A).

Over the opt100 dataset, performance was fairly robust over a range of parameter values.

We have found that for the model GWL∼ΔTWS, for both training and test dataset, performances of SVR and ANN are better than that of LRM in terms of ρ and RMSE.

This paper will help researchers to investigate the previous studies from metrics, methods, datasets, performance evaluation metrics, and experimental results perspectives in an easy and effective manner.

"Experiments and results" describes the experimental settings, datasets, performance measures, and testing results.

13321_2018_260_MOESM2_ESM.xlsx Additional file 2. Information about the applied datasets, performance of the predictive models, and evaluation of the gain- cost function for the different datasets and settings.

Naïve Bayes method was found to be the least affected method achieving in several tested datasets performance higher than 0.6 MCC even at the highest level of noise tested 50% and outperforming more complex methods.