Understand the idea
Multiple regression fits several coefficients together. Each coefficient describes a conditional comparison with the other model inputs held constant.
Python skill: Passes two feature columns in a fixed order.
Meet the syntax
LinearRegression().fit(X_train[['distance','weight']], y_train)X_train[['distance','weight']]- Passes two feature columns in a fixed order.
LinearRegression().fit- Learns both slopes together, making each coefficient conditional on the other supplied inputs.
Follow the code
Use the numbered comments to connect each Python block to the workflow above.
answer=pd.Series(model.coef_,index=['distance','weight'])
This practice: Read and run the Python. Next: Change · Several predictors, conditional comparisons.
Multiple linear regression · from idea to workflow
Question: How do several available inputs jointly predict a quantity?
Mechanism: One coefficient per prepared input adds to an intercept.
Watch for: Correlated inputs destabilise individual coefficients; coefficients do not establish causes.
Interpret or debug · self-review: Explain what holding other inputs fixed means. Would two near-duplicate inputs change coefficient interpretation even if predictions barely change?
Independent full workflow: ML-X03 · ML-X04. First practise complete regression and classification in F11–F12; check readiness in W-K2–W-K3.
Given data · MIX60
60 observations. One synthetic delivery observation generated for practice. The dataframe df is supplied afresh for each Run.
| distance | weight | service | weekend | duration |
|---|---|---|---|---|
| 15.7052 | 6.75035 | standard | 0 | 61.7704 |
| 9.33869 | 4.81674 | express | 1 | 35.1694 |
| 17.3134 | 5.73931 | economy | 0 | 59.0766 |
| 14.25 | 7.69699 | standard | 1 | 56.5785 |
| 2.78937 | 6.42024 | express | 0 | 19.5577 |
| 19.5368 | 5.62508 | economy | 1 | 77.0289 |
| 15.4617 | 5.68023 | standard | 0 | 56.8109 |
| 15.9352 | 3.17871 | express | 1 | 52.08 |
Column meanings and units
Distance, weight and duration use the fixture’s numeric units; no kilometres, kilograms, minutes or other physical units are specified. RMSE is reported in the same synthetic duration units as the target.
These deterministic teaching observations do not describe real deliveries. Service effects and the alternating weekend flag are built into the generated response; they do not establish real-world causal effects.
distancefloat64- Numeric delivery-distance inputUnit / values: Synthetic distance units; physical unit unspecified
weightfloat64- Numeric parcel-weight inputUnit / values: Synthetic weight units; physical unit unspecified
servicestr- Delivery-service categoryUnit / values: standard / express / economy
weekendint64- Binary weekend input, alternating in the fixtureUnit / values: 0 / 1 indicator
durationfloat64- Numeric delivery-duration targetUnit / values: Synthetic duration units; physical unit unspecified
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','weight','service','weekend']]
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[['distance','weight']],y_train)
Your task · Follow
Return named distance and weight coefficients from the supplied fitted two-feature line.
Hint 1 — Think
Coefficient labels must follow the order used when fitting the supplied two-feature model.
Hint 2 — Tools
model.coef_ and pd.Series with an index.
Hint 3 — Approach
Pair the fitted coefficients with distance and weight in training-column order.
Explained solution
answer=pd.Series(model.coef_,index=['distance','weight'])
Named coefficients preserve which conditional association each learned value describes.
Helpful prior knowledge: Read a fitted line 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.