Forcing sklearn cross val score to use stratified k fold?

Question

Based on Sklearn Docs:

• Is it possible to force the use of StratifiedKFold?
• How can I know which KFold has been used?

Clowers

Answer 1

Use this:

 cross_val_score(estimator, X, y, cv=YOURCHOICE)

Example:

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) 

Beamer

and how do you check if it is the method I wanted – Clowers

you cannot explicitly check this but it is guaranteed by the source code internally – Beamer

Answer 2

in the sklearn documentation wrote that:

"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."...

If your model is a classifier, just use an integer for using StratifiedKFold.

Nullifidian

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