数据挖掘 - 为什么树比增强分类器效果更好？ - 吾爱随笔录

我预测使用 scikit-learn 编码的 10 个类别标签，有 6 个因素，120 万个案例。DecisionTreeClassifier RandomForestClassifier ExtraTreesClassifier 提供 0.9 的准确度（以及精确度和召回率）

AdaBoostClassifier GradientBoostingClassifier 的精度为 0.2

关于巨大差异的任何指示？

（我正在做gridsearchcv）。代码：

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

def output_metrics():
    from sklearn.metrics import accuracy_score, precision_score, recall_score
    print("Accuracy:",accuracy_score(y_test, y_pred))
    print('Precision', precision_score(y_test, y_pred, average=None).mean())
    print('Recall', recall_score(y_test, y_pred, average=None).mean())


from sklearn.ensemble import AdaBoostClassifier
from sklearn.model_selection import GridSearchCV

tree_para =  { 'n_estimators': [16, 32] }
clf = GridSearchCV(AdaBoostClassifier(), tree_para, cv=5)
model= clf.fit(X_train, y_train)

y_pred = clf.predict(X_test) 

output_metrics()