Sentence examples for poor models with from inspiring English sources

Users of Version 1, in about half the cases, selected poor models with high forecast error.

Some datasets were poorly predicted, especially the two datasets 2326 and 485290 produced poor models with very low efficiency (0.395 and 0.51 respectively), likely due to the extreme imbalance in the ratio of active to inactive compounds, 0.37 and 0.28%, respectively (Table 2), in the training data.

For the different model qualities (a poor model with λ = 1 or a good model with λ = 20) and different cutoff values (χ = 1 or 10%), there is a significant dependency for the REF, PRE and ACC metrics on the R a value.

All metrics are model quality dependent, but the ROCE, EF, REF, MCC, CKC, SEN and PRE show an approximate tenfold increase when moving from a poor model with quality λ = 2 to a good model with quality λ = 40, while in the case of the PM metric a doubling of the parameter value is observed (going from PM = 0.5 for a poor model to a value of 0.98 for a good model; Table 1).

Our analysis supports a hydrogen-rich atmosphere with a cloud or haze layer, although a hydrogen-poor model with 10% water is not ruled out.

To work with poor models may be a choice of the designer to avoid, for example, slow controlled system responses or may be the result of lack of explicit information to construct better models.

Both organisms have many experimental advantages, but their very high fecundities [ 27, 28]--which can facilitate rapid rates of adaptation--make them poor models for vertebrate species with much lower reproductive rates.

Additionally, all multivariate regressions showed a poor model fit, with likelihood-based pseudo R-square values ranging from 0.01 to 0.05, with most falling near 0.02.

When comparing 12-km and 4-km grid resolutions for the PX simulation in CMAQ statistics analysis, the CMAQ results at 12-km grid resolution consistently show under predictions of 8-h O3 at both of valley and mountain areas and particularly, it shows relatively poor model performance with a 15.1% of NMB (Normalized Mean Bias).

(The method exhibited less-than-ideal performance in our simulation study, likely due to poor model fit with the simulated error structures).

Consequently, using just 1-kbp genomic regions as done previously [14], likely predict poor transcript models with high frequency.