Testing error

Figure 28 Overall training and testing error for all six properties predicted by the integrated model.

Test plans should include definite, measurable acceptance criteria, in addition to the amount of testing to be done. The test plan should include the data sets to be used and the detailed instructions for testing. Errors encountered during testing must be documented, including how they were discovered, their description, and any action taken to remedy the error. Errors must be remedied prior to the release of the software or computer system. Test results must be documented clearly to allow for pass/fail determinations to be made. 

A variety of factors may be responsible for apparent lack of response to therapy. It is possible that the disease is not infectious or nonbacterial in origin, or there is an undetected pathogen. Other factors include those directly related to drug selection, the host, or the pathogen. Laboratory error in identification and/or susceptibility testing errors are rare. 

Figure 11 Variation of training error (solid line) with testing error (dashed line) as a function of epoch during typical neural network training.

FIGURE 5.24 fc-NN classification for the glass data with six glass types. The optimal parameter for k, the number of nearest neighbors, is 2. The test error for this parameter choice is 0.34. 

Test error The classification rule is derived from the whole calibration set with a certain parameter choice. Then the mle is applied to the test set, and the test error is the resulting misclassification error of the test set. Note that in principle it would be sufficient to compute the test error only for the optimal parameter choice. 

Different selections of calibration and test data set may lead to different answers for the errors. In the following, we present results from one random split however, in the final overall comparison (Section 5.8.1.8) the evaluation scheme is repeated 100 times to get an idea of the distribution of the test error for the optimal parameter choice. 

Figure 5.24 shows the results for fc-NN classification for a range of k from 1 to 30. As mentioned above, the training error must be zero for k = 1, and it increases with k. The CV error is visualized by black dots for the means and vertical bars for mean plus/minus one standard error. The dotted horizontal line is drawn at the mean plus 1 standard error for the smallest mean CV error, and it is used for the selection of the optimal parameter (see Section 4.2.2). Accordingly, k = 2 is the optimal solution, most likely because of the small data groups. The resulting test error for k = 2 is 0.34. 

The most important parameter choices for SVMs (Section 5.6) are the specification of the kernel function and the parameter y controlling the priority of the size constraint of the slack variables (see Section 5.6). We selected RBFs for the kernel because they are fast to compute. Figure 5.27 shows the misclassification errors for varying values of y by using the evaluation scheme described above for k-NN classification. The choice of y = 0.1 is optimal, and it leads to a test error of 0.34. 

As mentioned above, the test errors depend on the selection of training (calibration) and test data sets. We can get an idea about the distribution of the test errors by... 

FIGURE 5.28 Comparison of the test errors for the glass data using different classification methods. One hundred replications of the evaluation procedure (described in the text) are performed for the optimal parameter choices (if the method depends on the choice of a parameter). The methods are LDA, LR, Gaussian mixture models (Mix), fc-NN classification, classification trees (Tree), ANN, and SVMs. 

Repeatability or the repeatability interval of a test (r) is the maximum permissible difference due to test error between two results obtained on the same material in the same laboratory. 

As mydriatic and cycloplegic agent Atropine is used to produce mydriasis and cycloplegia for testing errors of refraction. Mydriasis is required for fundoscopic examination and in the treatment of iritis and keratitis. 

The replication mean square, 0 075 in this case, is a measure of error variance The square root of this number is the standard deviation of experimental f test eTror if the experiment actually was repeated twice of test error, only, if the two results represent two analyses on each experiment. 

The 95% limits of the experimental method (or the test error depending on the design used) can be estimated by multiplying the estimated standard deviation by two, and adding and subtracting this from the experimental result ... 

Many knock-down, drag-out battles between experimenter and tester result when the test variation is considered too large. This topic is not worthy of discussion unless somewhere along the line the basic test error has been sought out, identified, minimized if possible, and published. In Chapters VI and VII, methods for pinning down the sources of test error ate discussed. 

Look for the most commonly tested errors. There are seven kinds of mistakes that pop up most often in Improving Sentences questions. If you are having trouble finding an error, do a quick check for the common errors described in this section. 

Fig. 20 Autopredictive, cross-validation and test errors for dataset B (acynaphthylene) and PLS1.

With today s R R, spectophotometers make up only a small portion of total measurement error, assuming they are diffuse spherical geometry and halogen flash-lamp source units. If you have a good spectrophotometer, then the majority of test error originates from sampling and test preparation. 

Incremental integration testing is the antithesis of the nonincremental approach. The system is tested as subsystems before being tested as collections of subsystems finally, the complete system is tested. Errors should be easier to isolate and correct predictably, interfaces should be easier to test more comprehensively, and it should be easier to develop a systematic test plan. 

This integer specifies the kind of norm to be used in testing error vectors. Set Info(16)=0 to request the infinity-norm (maximum absolute element). 

Whenever possible, toxicant concentration should also be taken at the beginning and end of the test. Errors in measurement, degradation, or voliti-zation can produce a concentration different from that of the expected or nominal concentration. 

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