________________________ [doctest] common_pitfalls.rst _________________________
218     0.46
219 
220 The pipeline can also be fed into a cross-validation
221 function such as :func:`~sklearn.model_selection.cross_val_score`.
222 Again, the pipeline ensures that the correct data subset and estimator
223 method is used during fitting and predicting::
224 
225     >>> from sklearn.model_selection import cross_val_score
226     >>> scores = cross_val_score(pipeline, X, y)
227     >>> print(f"Mean accuracy: {scores.mean():.2f}+/-{scores.std():.2f}")
Expected:
    Mean accuracy: 0.46+/-0.07
Got:
    Mean accuracy: 0.45+/-0.07