python
23 lines · 1 tab
Dr. Elena Vasquez
Apr 2026
1 tab
from sklearn.model_selection import StratifiedKFold, train_test_split, cross_validate
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
test_size=0.2,
stratify=y,
random_state=42,
)
pipeline = Pipeline([
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler()),
('model', LogisticRegression(max_iter=1000)),
])
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
scores = cross_validate(pipeline, X_train, y_train, cv=cv, scoring=['roc_auc', 'f1', 'precision', 'recall'])
print(scores)
1 file · python
Explain with highlit
Evaluation goes wrong when data splitting is treated like boilerplate. I stratify imbalanced targets, guard time order when necessary, and make sure preprocessing lives inside cross-validation. This is the difference between a model that looks good in a notebook and one that behaves predictably in production.
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