Sentence examples for get biased from inspiring English sources

"Most industries are not going to get biased for one side or another," Russ Choma, a money-in-politics reporter at the Center for Responsive Politics, told me.

Many traditional machine learning methods such as support vector machine and so on generally get biased classifier which leads to lower classification precision for minor class than major class.

Empirically, this is incorrect, however, because one would select the sample on the dependent variable and thus get biased estimates.

Because there are no APs that have CA center outside the building, the estimates near the edges of the building get biased towards the inside of the building.

This sends a clear message about why measurement matters: ask people for their perceptions and you'll get biased answers and disagreement.

Liverpool (and the rest of the 'big 4') get biased referee decisions go their way in the Premiership but Liverpool seem to get them all of the time in the CL too.

Undoubtedly, getting biased rhizosphere opens new opportunities for future agricultural developments based on exploiting the beneficial microbial services to reduce the inputs of agrochemicals, thereby reaching sustainable environmental and economical goals.

There is a large literature on the risks of getting biased estimates due to the potential reverse causality that is latent in ALMP analyses.

If these CAs are used as independent measurements, the estimate covariance becomes too optimistic and the estimate mean gets biased towards the MIMO CA mean.

However, l 1-SVD can work properly and get nearly unbiased results when distinguished signals are close to each other in α domain but well separated to each other in β domain while 2D- l 1-SVD gets biased results in the α domain.

Put differently, leave-one-out cross-validation for model assessment almost unbiasedly estimates the prediction error of the original data set of size n [68] while for smaller n train and larger n test the estimator gets biased as an estimator of PE(n) since it overestimates PE(n).