Sentences with phrase «based prediction error»

If the PLS method were able to minimize cross-validation based prediction error when forming each PLS component, rather than maximizing cross-covariance, then it probably would achieve a superior result (lower Spread ratio) when using all predictors simultaneously than just any one of them, but such a method would be extremely computationally demanding.

Not exact matches

Overall, the Mean Absolute Error (MAE) of social media based prediction was higher than 7.

The DNNs are based on predictive coding theory, which assumes that the internal models of the brain predict the visual world at all times and that errors between the prediction and the actual sensory input further refine the internal models.

This flexibility suggests finding a lower bound on the mean square error for linear prediction of from factors based on test scores.

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These phases, which last 30 years, giving a 60 - year cycle, must be carefully allowed for: otherwise the error made by many early models would arise: they based their predictions on the warming rate from 1976 - 2001, a period wholly within a warming phase of the Pacific Decadal Oscillation.

The errors in these parameters alone, much less scattering parameters for particulates, ice, etc. etc. would make any predictions based on models very insecure.

Are you referring to scientifically qualified predictions based on modeling where observations fall outside of error ranges?

It is that we haven't seen convincing evidence or arguments that don't appear to have been contrived, fudged, based on invalid calculation methods, or based on models (or proxies) that haven't been validated, by people who haven't owned up to past errors in prediction but are apparently continually rewriting history so that the latest weather calamity is suddenly discovered to have been predicted all along.

This is because the calibration errors are magnified for predictions based on proxy

The curved blue lines in Figure 9 - 1 present the calibration error, or the uncertainty in predictions based on the calibration (technically the 95 percent prediction interval, which has probability 0.95 of covering the unknown temperature), which is a standard component of a regression analysis.