Prediction accuracy

In recent decades, much attention has been paid to the application of artificial neural networks as a tool for spectral interpretation (see, e.g.. Refs. [104, 105]). The ANN approach app]ied to vibrational spectra allows the determination of adequate functional groups that can exist in the sample, as well as the complete interpretation of spectra. Elyashberg [106] reported an overall prediction accuracy using ANN of about 80 % that was achieved for general-purpose approaches. Klawun and Wilkins managed to increase this value to about 95% [107]. 

In hen of careful independent checks of predictive accuracy, the results of the comprehensive theoretical work will not be presented here. Simpler, more easily understood predictive methods, for certain important limiting cases, will be presented. As a check on the accuracy of these simpler methods, it will perhaps be prudent to calculate the bubble diameter from the graphical representation by Mersmann (loc. cit.) of the resiJts of Kumar et al. (loc. cit.). 

Two principal topics are considered under theory of sampling. First is theoiy accounting for physical properties of material to be sampled. Second is the process of mechanical sample extrac tion. The theoiy predicts accuracy of sample taking—how much sample to take and howto take it to meet an accuracy specification. 

Whether this tendency of PLS to reject nonlinearities by pushing them onto the later factors which are usually discarded as noise factors will improve or degrade the prediction accuracy and robustness of a PLS calibration as compared to the same calibration generated by PCR depends very much upon the specifics of the data and the application. If the nonlinearities are poorly correlated to the properties which we are trying to predict, rejecting them can improve the accuracy. On the other hand, if the rejected nonlinearities contain information that has predictive value, then the PLS calibration may not perform as well as the corresponding PCR calibration that retains more of the nonlinearities and therefore is able to exploit the information they contain. In short, the only sure way to determine if PLS or PCR is better for a given calibration is to try both of them and compare the results. 

Figure 6.31 compares the measured heat transfer coefficient by Lee and Mudawar (2005b) in two-phase flow of R-134a to predictions based on previous studies. The predictive accuracy of a correlation was measured by the mean absoiute error, defined as... 

Figure 3 gives two examples of L and L closeness of two functions. The L closeness leaves open the possibility that in a small region of the input space (with, therefore, small contribution to the overall error) the two functions can be considerably different. This is not the case for L closeness, which guarantees some minimal proximity of the two functions. Such a proximity is important when, as in this case, one of the functions is used to predict the behavior of the other, and the accuracy of the prediction has to be established on a pointwise basis. In these cases, the L error criterion (4) and its equivalent [Eq. (6)] are superior. In fact, L closeness is a much stricter requirement than L closeness. It should be noted that whereas the minimization of Eq. (3) is a quadratic problem and is guaranteed to have a unique solution, by minimizing the IT expected risk [Eq. (4)], one may yield many solutions with the same minimum error. With respect to their predictive accuracy, however, all these solutions are equivalent and, in addition, we have already retreated from the requirement to find the one and only real function. Therefore, the multiplicity of the best solutions is not a problem. 

However, there is still a strong need to develop new methods that will be able to quantitatively or at least qualitatively estimate the prediction accuracy of log D models. Such models will allow the computational chemist to distinguish reliable versus nonreliable predictions and to decide whether the available model is sufficiently accurate or whether experimental measurements should be provided. For example, when applying ALOGPS in the LIB RARY model it was possible to predict more than 50% and 30% compounds with an accuracy of MAE <0.35 for Pfizer and AstraZeneca collections, respectively [117]. This precision approximately corresponds to the experimental accuracy, s=0.4, of potentiometric lipophilicity determinations [15], Thus, depending on the required precision, one could skip experimental measurements for some of the accurately predicted compounds. 

H. van der Voet, Comparing the predictive accuracy of models using a simple randomization test. Chemom. Intell. Lab. Syst., 25 (1994) 313-323. 

Only a few models applicable to paddy field conditions have been developed. RICEWQ by Williams, PADDY by Inao and Kitamura," and PCPF-1 by Watanabe and Takagi are useful for paddy fields. EXAMS2 by the United States Environmental Protection Agency (USEPA), a surface water model, can also be used to simulate paddy fields with an appropriate model scenario and has been used for the prediction of sulfonylurea herbicide behavior in paddy fields. The prediction accuracy of PADDY and PCPF-1 is high, although these models require less parameter... 

PCPE-1 differs greatly from RICEWQ and PADDY in that the sediment layer is divided into an oxidative layer and a reductive layer because the 0-1-cm depth of sediment is oxidative, where most agrochemicals are adsorbed, and below 1 cm it is reductive. Agrochemical degradation can be different in the oxidative and reductive layers of the sediment. The prediction accuracy of agrochemical concentrations is improved sharply by this consideration. 

Some studies suggest that the predictive accuracy of this formula for women is better without the correction factor of 0.85. 

CHF correlation with uniform heating. A correlation for uniformly heated round ducts was proposed by A.R.S. (Clerici et al., 1967 Biasi et al., 1967, 1968). The correlation was claimed to combine a very simple analytical form with a wide range of validity and a great prediction accuracy. The correlation consists of two straight lines in the plane q"0, Xe ... 

The accuracies of the various PONDRs were estimated (Table IV) by applying them to the ordered sequences in 0 PDBS25 as summarized in Table II and to the merged set of disordered proteins described in Table I. Overall, the prediction accuracy of each PONDR was much better on the 222,116 ordered residues of 0 PDBS25 than on the 18,833 residues of the merged disorder set. Thus, prediction of order generalized much better than prediction of disorder. 

The first benchmark of a QSAR model is usually to determine the accuracy of the fit to the training data. However, because QSAR models are often used for predicting the activity of compounds that have not yet been synthesized, the most important statistical measures are those giving an indication of their prediction accuracy. Common methods to test QSAR predictivity are listed below. 

In a series of works, Nishikawa and colleagues have shown that intracellular and extracellular proteins can be distinguished by their amino acid composition. In their recent work, they also used the residue pair frequency (not only the neighboring ones, but also pairs with some spacers) and reported a considerable improvement in prediction accuracy (Nakashima and Nishikawa, 1994). 

Ladunga, I., Czako, F., Csabai, I., and Geszti, T. (1991). Improving signal peptide prediction accuracy by simulated neural network. Comput. Appl. Biosci. 7, 485-487. Landolt-Marticorena, C., Williams, K., Deber, C., and Reithmeier, R. (1993). Non-random distribution of amino acids in the ransmembrane segments of human type I single span membrane proteins. J. Mol. Biol. 229, 602-608. 

At the low ionic concentrations encountered in sour water strippers, the effect of dissolved ions is probably small. Thus at a 1% concentration of sodium acetate the volatility of ammonia only increases about 2.5% due to the salt. This is within the prediction accuracy of the ammonia volatility data and no correction is therefore required. However significant ionic effects could exist in the condenser where high concentrations of the ionic components could exist. 

A prediction accuracy of 10 to 30% is claimed. D. Models of Kumar, Kuloor, and Co-workers... 

Phillips AG, Ahn S, Lloresco SB. 2004. Magnitude of dopamine release in medial prefrontal cortex predicts accuracy of memory on a delayed response task. J Neurosci 24(2) 547-553. 

In the absence of an accurate determination of prediction errors, it was not possible to calculate a mismatch level specific to each compound, although a method was proposed by which it could if this situation were to change. Instead, a generic mismatch level needed to be adopted above which postulates were rejected and below which postulates were accepted. The problem in this situation is the large variation in prediction accuracy, e.g., two similar postulates of which only one is truly correct, but whose common parts are well predicted, will both yield a mismatch below a generic threshold. A mismatch criterion specific to each postulate, would be a pre-requisite for a calculation of the probability that the postulate was indeed correct. 

---