EL之GB(GBC)：利用GB对多分类问题进行建模(分层抽样+调1参)并评估

EL之GB(GBC)：利用GB对多分类问题进行建模(分层抽样+调1参)并评估

 

 

目录

输出结果

设计思路

核心代码

 

 

 

 

输出结果

 

T1、

T2、

 

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