Sentence examples for predictions was small from inspiring English sources

However, the proportion of false predictions was small (<10%%), and a 10-PS classifier predicted all compounds correctly for the 'leave-one-out' concept (Fig.  7a).

In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small.

In general, the differences between data and predictions are small.

However, differences in predictions are small with mean absolute difference at the state level of 0.069 and mean squared difference of 0.012.

The test statistics again indicate that the estimated coefficients are not equal and hence are not robust across subsets of the inpatient sample according to medical condition, but differences in predictions are small with highest mean absolute difference at the state level of 0.054 and highest mean squared error of 0.005.

Moreover, one can find that for larger infection rates (gamma_{1}) and (gamma_{2}), the error between stochastic simulation and theoretical predictions is smaller, which is likely to be attributed to the contacts homogeneity between individuals and vectors.

For example, Breidenbach et al. (2010) used the k-nn technique to predict standing timber volume from airborne laser scanning data and ground-based survey data and found that standard errors of predictions were smaller at the stand level than the plot level and smaller for stands of 4 6 ha compared to stands of less than 2 ha.

We can observe that the model predictions are less sensitive to the error in the data when regularization is applied, i.e. the variance of the model predictions are smaller.

Mean square error of prediction was smaller when IVDMD was adjusted to in vivo values using an equation derived from the average of all in vitro runs compared to the average mean square error of prediction from the 21 individual runs.

The RMS errors of the prediction were small, and the computation time for the new, optimal objective function is an order of magnitude less than for existing approaches.

Linear Discriminant Analysis (LDA) is a long-standing prediction method that has been well characterized when the number of features used for prediction is small [1].