Understand the idea
Cross-validation repeatedly holds away part of the training population. The final test is outside this process. Each fold fits a fresh estimator.
Python skill: Produces positional training/validation index pairs; it does not fit a model.
Meet the syntax
KFold(n_splits=5, shuffle=True, random_state=42)
folds.split(X_train)KFold- Produces positional training/validation index pairs; it does not fit a model.
n_splits=5- Divides the supplied training population into five validation folds.
shuffle=True- Shuffles ordinary unordered observations before partitioning.
random_state=42- Makes the shuffled folds repeatable.
folds.split(X_train)- Yields positional training and validation indices for the supplied development table.
Follow the code
Use the numbered comments to connect each Python block to the workflow above.
from sklearn.model_selection import KFold
answer=list(KFold(3,shuffle=True,random_state=42).split(X_train))
This practice: Read and run the Python. Next: Change · What a fold does.
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)
Your task · Follow
Use three shuffled KFold splits with seed 42 on X_train. Store the three (training positions, validation positions) pairs in answer.
Hint 1 — Think
Fold indices describe roles within the training table; they are not new observations.
Hint 2 — Tools
KFold.split and a list of index-array pairs.
Hint 3 — Approach
Construct the requested shuffled three-fold splitter and inspect every pair returned for X_train.
Explained solution
from sklearn.model_selection import KFold
answer=list(KFold(3,shuffle=True,random_state=42).split(X_train))
Each pair separates a fitting subset from a validation subset without involving the reserved test population.
Helpful prior knowledge: Learn missing-value replacements safely 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.