当我运行此代码时:
from sklearn import cross_validation
bs = cross_validation.Bootstrap(9, random_state=0)
我收到了这个弃用警告:
C:\Anaconda\envs\p33\lib\site-packages\sklearn\cross_validation.py:684: DeprecationWarning: Bootstrap will no longer be supported as a cross-validation method as of version 0.15 and will be removed in 0.17
"will be removed in 0.17", DeprecationWarning)
我应该使用什么而不是引导程序?
答案 0 :(得分:5)
来自the scikit-learn 0.15 release notes," API更改摘要"
cross_validation.Bootstrap
已弃用。建议使用cross_validation.KFold
或cross_validation.ShuffleSplit
。
# See, e.g., http://youtu.be/BzHz0J9a6k0?t=9m38s for a motivation
# behind this deprecation
warnings.warn("Bootstrap will no longer be supported as a " +
"cross-validation method as of version 0.15 and " +
"will be removed in 0.17", DeprecationWarning)
答案 1 :(得分:1)
您可以使用BaggingClassifier
:
bag = BaggingClassifier(base_estimator=your_estimator,
n_estimators=100,
max_samples=1.0,
bootstrap=True,
n_jobs=-1)
bag.fit(X, y)
recalls = []
for estimator, samples in zip(bag.estimators_, bag.estimators_samples_):
# compute predictions on out-of-bag samples
mask = ~samples
y_pred = estimator.predict(X[mask])
# compute some statistic
recalls.append(recall(y[mask], y_pred))
# Do something with stats, e.g. find confidence interval
print(np.percentile(recalls, [2.5, 97.5]))
答案 2 :(得分:0)
我刚刚遇到了这个问题,我找到的解决方案(从 scikit-learn 0.24 开始)是使用 resample 实用程序。
from sklearn.utils import resample
这将在每次调用时生成 1 个引导步骤,使用带替换采样的默认参数。
https://scikit-learn.org/stable/modules/generated/sklearn.utils.resample.html