Data for female heart attack patients in .NET Use pdf417 in .NET Data for female heart attack patients

How to generate, print barcode using .NET, Java sdk library control with example project source code free download:
11.5 Data for female heart attack patients use .net framework barcode pdf417 writer toincoporate pdf 417 on .net ISO Standards A B Inf 1.0 Complexity parameter Inf 0.261 0.048 Complexity parameter 0.261 0.048 Xval relative error Relative error 1 2 3 No. of splits 1 2 3 No. of splits Figure 11.7 While the re lative error (panel A) must inevitably decrease as the number of splits increases, the cross-validation relative error (X-val relative error, shown in panel B) is likely, once the number of splits is large enough, to increase. Results from three cross-validation runs are shown.

Plots are for the car mileage data of Figure 11.5 and Table 11.4.

. In summary: r The parame .NET barcode pdf417 ter cp is a proxy for the number of splits. For the initial rpart model, it should be set small enough that the cross-validation error rate achieves a minimum.

r Having identi ed the optimal tree (with the minimum cross-validation error rate), later splits are then pruned off.. 11.4.3 Prediction error versus tree size In Figure 11.7, we retur Visual Studio .NET PDF-417 2d barcode n to the car mileage data of Figure 11.

5 and Table 11.4. Figures 11.

7A and B plot, against tree size, different assessments of the relative error. As we have a continuous outcome variable (Mileage), the relative error is the sum of squares of residual divided by the total sum of squares. Figure 11.

7A plots the resubstitution assessment of relative error, which shows the performance of the tree-based prediction on the data used to form the tree. Figure 11.7B shows estimates from three cross-validation runs, with the data split into k = 10 subsets (or folds) at each run.

The plot gives an indication of the variability that can be expected, for these data, from one cross-validation run to another. The optimal tree, in Figure 11.7, is the smallest tree that has near minimum cross-validated relative error.

In Figure 11.7, the three runs suggest different choices for the optimal tree size. The defaults for the arguments minsplit and minbucket (see help(rpart.

control)) have limited the number of splits to 4. Between 2 and 4 splits may be optimal. Note the importance of doing several cross-validation runs.

11.5 Data for female heart attack patients This and the next section will proceed to look in detail at trees from substantial data sets. This section will examine data on the outcome (live or dead) for female heart attack.

Tree-based classi cation and regression patients, in the data fr .net framework pdf417 ame mifem (DAAG). The data are for the mortality of 1295 female heart attack patients.

A summary of the data follows.. > summary(mifem) outc ome live:974 dead:321 # data frame mifem (DAAG) yronset premi Min. :85.0 y :311 1st Qu.

:87.0 n :928 Median :89.0 nk: 56 Mean :88.

8 3rd Qu.:91.0 Max.

:93.0 angina stroke y :472 y : 153 n :724 n :1063 nk: 99 nk: 79 smstat c :390 x :280 n :522 nk:103. age Min. :35.0 1st Qu.

:5 .net framework pdf417 2d barcode 7.0 Median :63.

0 Mean :60.9 3rd Qu.:66.

0 Max. :69.0 diabetes highbp hichol y :248 y :813 y :452 n :978 n :406 n :655 nk: 69 nk: 76 nk:188 Notes:.

premi = previous myocard ial infarction event For smstat, c = current x = ex-smoker n = non-smoker nk = not known. (Technically, these are VS .NET barcode pdf417 patients who have suffered a myocardial infarction. Data are from the Newcastle (Australia) center of the Monica project; see the web site given under help(monica).

) In order to t the tree, we specify. mifem.rpart <- rpart( outcome ., method="class", data = mifem, cp = 0.

0025). The dot (.) on the right pdf417 for .NET -hand side of the formula has the effect of including all available explanatory variables.

A choice of cp = 0.0025 continues splitting to the point where the cross-validated relative error has started to increase. Figure 11.

8 shows the change in cross-validated error rate as a function of tree size, while the same information is shown alongside in printed form. The code is:. plotcp(mifem.rpart) # Cr oss-validated error vs cp printcp(mifem.rpart) # Tabular version of the same information.

Notice the increase in t pdf417 2d barcode for .NET he cross-validated error rate when there are more than two splits. For this tree, the optimum is a single split, i.

e., two leaves. In order to prune the tree back to this size, specify:.

mifemb.rpart <- prune (mifem.rpart, cp=0.

03). The cross-validated erro barcode pdf417 for .NET r rate is 0.248 0.

832 = 0.21. For these data, the optimum tree is very simple indeed, with a single split.

Figure 11.9 shows the tree, obtained using:.
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