Understand the idea
Out-of-fold predictions allow training-only diagnostic plots. After parameter selection they remain development evidence, not an unbiased substitute for final evaluation.
Python skill: Returns one held-out prediction for each training row, restoring the original row order.
Meet the syntax
cross_val_predict(model, X_train, y_train, cv=folds)cross_val_predict- Returns one held-out prediction for each training row, restoring the original row order.
cv=folds- Ensures each prediction comes from a fit that excluded that row.
Follow the code
Use the numbered comments to connect each Python block to the workflow above.
from sklearn.model_selection import cross_val_predict
answer=cross_val_predict(model,X_train,y_train,cv=folds)
This practice: Read and run the Python. Next: Change · Diagnose without opening the final test.
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
Create out-of-fold predictions for every training row.
Hint 1 — Think
Every diagnostic training prediction should come from a model that did not fit that row.
Hint 2 — Tools
cross_val_predict with the declared folds.
Hint 3 — Approach
Generate held-out predictions for the training population and preserve their returned order.
Explained solution
from sklearn.model_selection import cross_val_predict
answer=cross_val_predict(model,X_train,y_train,cv=folds)
Out-of-fold predictions support training-only diagnosis without reusing in-sample fitted predictions or opening the final test.
Helpful prior knowledge: How a search makes a choice 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.