Cross validation in Python

One split is a coin toss; cross validation scores every fold and averages.

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

X, y = load_iris(return_X_y=True)
model = make_pipeline(StandardScaler(), LogisticRegression(max_iter=500))

scores = cross_val_score(model, X, y, cv=5, scoring="accuracy")
print(scores.round(3))
print(f"{scores.mean():.3f} +/- {scores.std():.3f}")

How it works

1. cross_val_score fits a fresh clone of the estimator per fold.
2. cv=5 means five folds, so five scores come back.
3. The mean and the spread together tell you what to trust.

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Step 1 of 4 in Validation, step 8 of 25 in Machine learning with scikit-learn.

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