我想在多类梯度提升分类器的训练过程中监控损失,以了解是否发生过拟合。这是我的代码:
%matplotlib inline
import numpy as np
#import matplotlib.pyplot as plt
import matplotlib.pylab as plt
from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor
iris = datasets.load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
n_est = 100
clf = GradientBoostingClassifier(n_estimators=n_est, max_depth=3, random_state=2)
clf.fit(X_train, y_train)
test_score = np.empty(len(clf.estimators_))
for i, pred in enumerate(clf.staged_predict(X_test)):
test_score[i] = clf.loss_(y_test, pred)
plt.plot(np.arange(n_est) + 1, test_score, label='Test')
plt.plot(np.arange(n_est) + 1, clf.train_score_, label='Train')
plt.show()
但是我收到以下值错误:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-33-27194f883893> in <module>()
22 test_score = np.empty(len(clf.estimators_))
23 for i, pred in enumerate(clf.staged_predict(X_test)):
---> 24 test_score[i] = clf.loss_(y_test, pred)
25 plt.plot(np.arange(n_est) + 1, test_score, label='Test')
26 plt.plot(np.arange(n_est) + 1, clf.train_score_, label='Train')
C:\Documents and Settings\Philippe\Anaconda\lib\site-packages\sklearn\ensemble\gradient_boosting.pyc in __call__(self, y, pred)
396 Y[:, k] = y == k
397
--> 398 return np.sum(-1 * (Y * pred).sum(axis=1) +
399 logsumexp(pred, axis=1))
400
ValueError: operands could not be broadcast together with shapes (45,3) (45)
我知道如果我使用 GradientBoostingRegressor,这段代码可以正常工作,但我不知道如何让它与多类分类器(如 GradientBoostingClassifier)一起工作。谢谢你的帮助。
最佳答案
好像loss_
需要一个形状数组 n_samples, k
, 而 staged_predict
返回一个形状数组 [n_samples]
(根据文档)。您可能希望传入 staged_predict_proba
的结果或 staged_decision_function
进入 loss_
.
我认为您可以像这样测量训练集和测试集的损失:
for i, pred in enumerate(clf.staged_decision_function(X_test)):
test_score[i] = clf.loss_(y_test, pred)
for i, pred in enumerate(clf.staged_decision_function(X_train)):
train_score[i] = clf.loss_(y_train, pred)
plot(test_score)
plot(train_score)
legend(['test score', 'train score'])
请注意我第二次调用
loss_
我通过了火车组。输出看起来像我所期望的:关于python-2.7 - 如何绘制多类分类器中是否发生过拟合,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/23499423/