Data Science Desktop Survival Guide by Graham Williams
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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 cp= from this table.