## 20.23 Complexity Parameter Table

We can look at the raw data to have a more precise and detailed view of the data. Here we only list specific rows from the complexity parameter table.

tmodel$cptable[c(1:5,22:29, 80:83),] ## CP nsplit rel error xerror xstd ## 1 1.453731e-01 0 1.0000000 1.0000000 0.004616611 ## 2 3.460366e-02 1 0.8546269 0.8546269 0.004349444 ## 3 6.877579e-03 3 0.7854195 0.7894112 0.004214897 ## 4 4.814305e-03 4 0.7785420 0.7856353 0.004206800 ## 5 3.506217e-03 8 0.7592847 0.7633573 0.004158311 ## 22 5.394180e-04 40 0.7199342 0.7314238 0.004086605 ## 23 5.259325e-04 41 0.7193948 0.7308574 0.004085309 ## 24 5.214374e-04 52 0.7130566 0.7302910 0.004084012 ## 25 5.124471e-04 55 0.7114923 0.7302640 0.004083951 ## 26 4.854762e-04 57 0.7104674 0.7300752 0.004083518 ## 27 4.719907e-04 60 0.7090110 0.7294279 0.004082035 ## 28 4.585053e-04 64 0.7071230 0.7294279 0.004082035 ## 29 4.315344e-04 66 0.7062060 0.7285649 0.004080055 ## 80 8.630687e-05 791 0.5906627 0.7232246 0.004067761 ## 81 8.540784e-05 807 0.5888017 0.7235213 0.004068446 ## 82 8.091270e-05 815 0.5880735 0.7241686 0.004069940 ## 83 7.754133e-05 1012 0.5709469 0.7263533 0.004074973 %$

See how the relative error continues to decrease as the tree becomes more complex, but the cross validated error decreases and then starts to increase! We might choose a sensible value of from this table.

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