Understand the idea
predict uses fitted parameters. New rows must provide the same feature meaning and order. A one-row dataframe remains two-dimensional.
Python skill: Uses the fitted model without learning new parameters.
Meet the syntax
model.predict(pd.DataFrame({'distance':[4,7]}))model.predict- Uses the fitted model without learning new parameters.
pd.DataFrame({'distance':[4,7]})- Builds two new rows with the same feature name and two-dimensional shape used in fitting.
Follow the code
Use the numbered comments to connect each Python block to the workflow above.
answer=model.predict(pd.DataFrame({'distance':[4,7]}))
This practice: Read and run the Python. Next: Change · Predicting new 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)
from sklearn.linear_model import LinearRegression
model = LinearRegression().fit(X_train,y_train)
predictions = model.predict(X_test)
Your task · Follow
Predict durations for distance 4 and 7; store answer.
Hint 1 — Think
Prediction rows must use the feature name and shape the fitted line expects.
Hint 2 — Tools
pd.DataFrame and model.predict.
Hint 3 — Approach
Build the two requested distance rows, pass them through the supplied fitted model and retain their order.
Explained solution
answer=model.predict(pd.DataFrame({'distance':[4,7]}))
Prediction applies the existing learned line; it does not train a new model on the requested distances.
Helpful prior knowledge: What fitting does 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.