The phrase "average log score" is correct and usable in written English.
It can be used in contexts related to statistics, data analysis, or performance evaluation, particularly when discussing the mean of logarithmic scores.
Example: "The average log score of the model indicates its overall predictive performance across the dataset."
Alternatives: "mean log score" or "typical log score".
The "Best" model is the one that has had the highest average predictive log score up to t. "Memory 1" only has a memory effect from the previous year and an edges term.
On the basis of two to four preliminary runs with varying cycles (1.0 3.0 × 10), we estimated average log likelihood scores at stationarity (123nRTn ≈ -165,180; 12n3rRTn ≈ -106,860), and subsequently conducted two independent runs for each data set.
The average log-likelihood score (Fig. 5) improves significantly when the number of states grows from 1 to 20.
Therefore, the average log-likelihood score levels off when the number of states in the model surpasses 3. To further validate our models, we used both A3 and M6 to compute MFPTs between the α R and β/ C5 regions of the conformation space.
Figure 3 plots the average log-likelihood scores of all the models constructed.
Proliferation score for a sample is computed as the average log expression of these 43 genes in that sample.
But he points out that concrete logs don't require the cutting of trees, whereas construction of the average log home requires 100 large logs.
Total model score (log score) versus wall clock time.
This score represents the deviation of the average of the normalized log ratios from its expected value of zero and is proportional to the height, h (absolute average log ratio), of the genomic interval and to the square root of the number of probes in the interval.
The score is the average log-likelihood of a model for a single transition step along a trajectory.
Instead of median values, NetMHCcons uses the average log-transformed values as the final prediction scores.
