我想比较 adaboost 和决策树。作为原理证明，我将 adaboost 中的 estimators 数量设置为 1，默认为决策树分类器，期望得到与简单决策树相同的结果。
我确实在预测我的测试标签方面获得了相同的准确性。但是，adaboost 的拟合时间要短得多，而测试时间要长一些。 Adaboost 似乎使用与 DecisionTreeClassifier 相同的默认设置，否则，准确性不会完全相同。
谁能解释一下？
代码
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
print("creating classifier")
clf = AdaBoostClassifier(n_estimators = 1)
clf2 = DecisionTreeClassifier()
print("starting to fit")
time0 = time()
clf.fit(features_train,labels_train) #fit adaboost
fitting_time = time() - time0
print("time for fitting adaboost was", fitting_time)
time0 = time()
clf2.fit(features_train,labels_train) #fit dtree
fitting_time = time() - time0
print("time for fitting dtree was", fitting_time)
time1 = time()
pred = clf.predict(features_test) #test adaboost
test_time = time() - time1
print("time for testing adaboost was", test_time)
time1 = time()
pred = clf2.predict(features_test) #test dtree
test_time = time() - time1
print("time for testing dtree was", test_time)
accuracy_ada = accuracy_score(pred, labels_test) #acc ada
print("accuracy for adaboost is", accuracy_ada)
accuracy_dt = accuracy_score(pred, labels_test) #acc dtree
print("accuracy for dtree is", accuracy_dt)
输出
('time for fitting adaboost was', 3.8290421962738037)
('time for fitting dtree was', 85.19442415237427)
('time for testing adaboost was', 0.1834099292755127)
('time for testing dtree was', 0.056527137756347656)
('accuracy for adaboost is', 0.99089874857792948)
('accuracy for dtree is', 0.99089874857792948)
最佳答案
我试图在 IPython 中重复你的实验，但我没有看到这么大的区别:
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
import numpy as np
x = np.random.randn(3785,16000)
y = (x[:,0]>0.).astype(np.float)
clf = AdaBoostClassifier(n_estimators = 1)
clf2 = DecisionTreeClassifier()
%timeit clf.fit(x,y)
1 loop, best of 3: 5.56 s per loop
%timeit clf2.fit(x,y)
1 loop, best of 3: 5.51 s per loop
尝试使用分析器，或先重复实验。
关于python - 为什么带有 1 个估计器的 adaboost 比简单的决策树更快？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/40563504/