实验性：对训练数据的子集使用交叉验证，计算您要考虑的每个选项的性能。然后选择最佳选项并使用此选项训练最终模型。

// different settings for hyper-parameters, 
// for instance different pruning criteria:
hpSet = { hp1, hp2, ...}  

trainSet, testSet = split(data)

for each hp in hpSet:
    // run cross-validation over 'train' using hyper-parameter 'hp' 
    // and store resulting performance
    perf[hp] = runCV(k, trainSet, hp)

bestHP = pick maximum hp in 'perf'
model = train(trainSet, bestHP)
perf = test(model, testSet)