How to implement cross_val_score with BayesianRidge using an unsupervised learning?

Question

I am new with sklearn .
My objective is to estimate the score of a dataset using cross_val_score with BayesianRidge estimator. It should be implemented using an unsupervised learning . The code below is taken from sklearn except that the target variable , y , is excluded.
The data is taken from sklearn.datasets import fetch_california_housing .

estimator = BayesianRidge()
score_full_data = pd.DataFrame(cross_val_score(br_estimator, X=X, y=None, scoring='neg_mean_squared_error', cv=5), columns=['Data'])

I got a TypeError: fit() missing 1 required positional argument: 'y' .
The expected result is:

Data
0   -0.408433
1   -0.636009
2   -0.614910
3   -1.089616
4   -0.407541

How is the correct way to do it?

Answer 1

It's not working because of the fact that you are using a supervised learning classifier and trying to use it as an unsupervised classifier. You can't simply expect the underlying implementation of BayesianRidge classifier to change just because you are not supplying the target variable, ie y . If you check the documentation here , you will see that y is not an optional argument. Image from the link for reference:

Image Source

Secondly, this is not an unsupervised learning problem in the first place. This dataset you mentioned is for regression. So it doesn't make sense to use unsupervised learning here.