强制sklearn cross val score使用分层k折？
Forcing sklearn cross val score to use stratified k fold?
基于Sklearn Docs：
- 是否可以强制使用
StratifiedKFold？
- 我怎么知道哪个
KFold被使用了？
使用这个：
cross_val_score(estimator, X, y, cv=YOURCHOICE)
示例：
from sklearn import datasets, linear_model
from sklearn.model_selection import cross_val_score
diabetes = datasets.load_diabetes()
from sklearn.model_selection import StratifiedKFold
X = diabetes.data[:150]
y = diabetes.target[:150]
lasso = linear_model.Lasso()
skf = StratifiedKFold(n_splits=2)
results = cross_val_score(lasso, X, y, cv=skf)
"For int/None inputs, if the estimator is a classifier and y is either binary or
multiclass, StratifiedKFold is used. In all other cases, KFold is
used."...
如果您的模型是分类器，只需使用整数即可 StratifiedKFold。
基于Sklearn Docs：
- 是否可以强制使用
StratifiedKFold？ - 我怎么知道哪个
KFold被使用了？
使用这个：
cross_val_score(estimator, X, y, cv=YOURCHOICE)
示例：
from sklearn import datasets, linear_model
from sklearn.model_selection import cross_val_score
diabetes = datasets.load_diabetes()
from sklearn.model_selection import StratifiedKFold
X = diabetes.data[:150]
y = diabetes.target[:150]
lasso = linear_model.Lasso()
skf = StratifiedKFold(n_splits=2)
results = cross_val_score(lasso, X, y, cv=skf)
"For
int/Noneinputs, if the estimator is a classifier andyis either binary or multiclass,StratifiedKFoldis used. In all other cases,KFoldis used."...
如果您的模型是分类器，只需使用整数即可 StratifiedKFold。