python - 为什么带有 1 个估计器的 adaboost 比简单的决策树更快？

Question

我想比较 adaboost 和决策树。作为原理证明，我将 adaboost 中的估计器数量设置为 1，并将决策树分类器作为默认值，期望得到与简单决策树相同的结果。

我在预测测试标签时确实获得了相同的准确度。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)

Answer 1

我试图在 IPython 中重复您的实验，但我没有看到如此大的差异:

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，或者先重复实验。