输出结果

设计思路

核心代码

#T1、

nEst = 500

depth = 3

learnRate = 0.003

maxFeatures = None

subSamp = 0.5

#T2、

# nEst = 500

# depth = 3

# learnRate = 0.003

# maxFeatures = 3

# subSamp = 0.5

glassGBMModel = ensemble.GradientBoostingClassifier(n_estimators=nEst, max_depth=depth,

                                                        learning_rate=learnRate, max_features=maxFeatures,

                                                        subsample=subSamp)

glassGBMModel.fit(xTrain, yTrain)

missClassError = []

missClassBest = 1.0

predictions = glassGBMModel.staged_decision_function(xTest)

for p in predictions:

   missClass = 0

   for i in range(len(p)):

       listP = p[i].tolist()

       if listP.index(max(listP)) != yTest[i]:

           missClass += 1

   missClass = float(missClass)/len(p)

   missClassError.append(missClass)

   if missClass < missClassBest:

       missClassBest = missClass

       pBest = p