EL之GB(GBC)：利用GB对二分类问题进行建模并评估

输出结果

T1、纯GB算法

T2、以RF为基学习器的GB算法

设计思路

核心代码

# nEst = 2000

# depth = 3

# learnRate = 0.007

# maxFeatures = None

nEst = 2000

depth = 3

learnRate = 0.007

maxFeatures = 20

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

                                                        learning_rate=learnRate,

                                                        max_features=maxFeatures)

rockVMinesGBMModel.fit(xTrain, yTrain)

auc = []

aucBest = 0.0

predictions = rockVMinesGBMModel.staged_decision_function(xTest)

for p in predictions:

   aucCalc = roc_auc_score(yTest, p)

   auc.append(aucCalc)

   if aucCalc > aucBest:

       aucBest = aucCalc

       pBest = p