Understand the idea
A random state makes this split reproducible. It does not make a poor study design valid. Split before learning preprocessing statistics or model parameters.
Python skill: Splits features and targets together so row alignment is preserved.
Meet the syntax
train_test_split(X, y, test_size=0.2, random_state=42)train_test_split(X, y- Splits features and targets together so row alignment is preserved.
test_size=0.2- Reserves 20% of rows for the test partition.
random_state=42- Makes this random partition reproducible; it does not guarantee a representative sample.
Follow the code
Use the numbered comments to connect each Python block to the workflow above.
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)
This practice: Read and run the Python. Next: Change · Making a reproducible split.
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 |
Your task · Follow
Split distance/duration into 80% training and 20% test with seed 42.
Hint 1 — Think
Splitting X and y separately risks breaking which outcome belongs to which row.
Hint 2 — Tools
train_test_split, test_size and random_state.
Hint 3 — Approach
Select the legitimate input and target, split them in one call and retain all four returned objects.
Explained solution
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)
A joint split preserves alignment and a fixed seed makes the declared evaluation boundary reproducible.
Helpful prior knowledge: Seen is not unseen 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.