Understand the idea
StandardScaler learns means and scales during fit; transform reuses them. In supervised prediction learn them on training rows. In discovery fit them on the declared exploratory population, as U02 explains. You can enter here directly from Foundations.
Python skill: Learns a mean and scale from each training feature column.
Meet the syntax
scaler.fit(X_train)
scaler.transform(X_new)
StandardScaler()scaler.fit(X_train)- Learns a mean and scale from each training feature column.
scaler.transform(X_new)- Applies the already learned scale; it does not refit on the new rows.
StandardScaler()- Creates an unfitted scaler; it learns statistics only when fit is called.
Follow the code
Use the numbered comments to connect each Python block to the workflow above.
from sklearn.preprocessing import StandardScaler
scaler=StandardScaler().fit(X_train)
answer=scaler.transform(X_train)
This practice: Read and run the Python. Next: Change · Learn a scale from training rows.
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
Fit a scaler on the supplied training distance column; store its transformed values.
Hint 1 — Think
A scale is learned information, not merely a cosmetic unit conversion.
Hint 2 — Tools
StandardScaler.fit and transform.
Hint 3 — Approach
Fit on the supplied training column, then apply that fitted scaler to those rows.
Explained solution
from sklearn.preprocessing import StandardScaler
scaler=StandardScaler().fit(X_train)
answer=scaler.transform(X_train)
The mean and spread come exclusively from the declared fitting population.
Helpful prior knowledge: ML Foundations checkpoint 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.