JuniorTheoryCommon

What does this program print?

Python

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score

X, y = load_iris(return_X_y=True)
scores = cross_val_score(LogisticRegression(max_iter=1000), X, y, cv=4)
print(scores.shape)

(4,)

Expecting (), one averaged score: cross_val_score returns one score per fold, so cv=4 gives an array of 4 values; the mean is taken with .mean().
Expecting (150,), a score per row: the held-out rows of a fold are scored together as one accuracy, so the array has one value per fold, not per sample.

How would you report these four scores in one line, and why does their spread matter?
Why are the class shares the same in every fold when cv=4 is passed for a classifier?

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JuniorTheoryCommon

What does this program print?

Python

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score

X, y = load_iris(return_X_y=True)
scores = cross_val_score(LogisticRegression(max_iter=1000), X, y, cv=4)
print(scores.shape)

(4,)

Expecting (), one averaged score: cross_val_score returns one score per fold, so cv=4 gives an array of 4 values; the mean is taken with .mean().
Expecting (150,), a score per row: the held-out rows of a fold are scored together as one accuracy, so the array has one value per fold, not per sample.

How would you report these four scores in one line, and why does their spread matter?
Why are the class shares the same in every fold when cv=4 is passed for a classifier?

Back to questions Open topic in roadmap →Answer it yourself — Quiz