Understand the idea
sklearn scorers follow “larger is better”, so RMSE scoring is negated. The test_score key returned by cross_validate refers to fold validation, not the final test. A dummy reference uses the same folds.
Python skill: Fits independent copies on the training part of each fold and returns timing/score arrays.
Meet the syntax
cross_validate(model, X_train, y_train, cv=folds, scoring='neg_root_mean_squared_error')cross_validate- Fits independent copies on the training part of each fold and returns timing/score arrays.
cv=folds- Reuses the declared fold design across comparisons.
scoring='neg_root_mean_squared_error'- Uses negative RMSE so larger scores are better; negate test_score to report positive RMSE.
Follow the code
Use the numbered comments to connect each Python block to the workflow above.
answer=cross_validate(model,X_train,y_train,cv=folds,scoring='neg_root_mean_squared_error')
This practice: Read and run the Python. Next: Change · Read cross-validation evidence.
Given data · LINE24
24 observations. Deterministic teaching observations; values illustrate the concept rather than a real population claim. The dataframe df is supplied afresh for each Run.
| distance | duration |
|---|---|
| 1 | 5.30501 |
| 1.47826 | 11.2361 |
| 1.95652 | 11.8488 |
| 2.43478 | 14.809 |
| 2.91304 | 10.7589 |
| 3.3913 | 19.4972 |
| 3.86957 | 17.89 |
| 4.34783 | 15.531 |
Column meanings and units
Column names describe the supplied features and target. Keep the stated units and row identities when making comparisons.
Synthetic data are deliberately small and reproducible. Their patterns illustrate an idea; they are not evidence about a real population.
| Column | Stored type |
|---|---|
| distance | float64 |
| duration | float64 |
Supplied setup · available if you need to inspect it
This code runs before your editor on every Run. These are the objects your exercise uses.
from sklearn.model_selection import train_test_split
X = df[['distance']]
y = df['duration']
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=.2,random_state=42)
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import KFold,cross_validate
folds=KFold(5,shuffle=True,random_state=42)
model=LinearRegression()
Your task · Follow
Cross-validate the supplied linear model and store the result dictionary.
Hint 1 — Think
Cross-validation returns separate evidence for each fit rather than one final-test score.
Hint 2 — Tools
cross_validate and the negative-RMSE scorer.
Hint 3 — Approach
Evaluate the supplied model on the declared training folds and retain the result dictionary.
Explained solution
answer=cross_validate(model,X_train,y_train,cv=folds,scoring='neg_root_mean_squared_error')
Independent fold fits provide comparable held-out training evidence while leaving the final test unused.
Helpful prior knowledge: What a fold does · A useful reference These links are guidance, not locks.
Sources and API context
Examples run with this Playground’s scikit-learn 1.4.2 / Pyodide 0.26.4 runtime.