Understand the idea
Settings control fitting; learned parameters are estimated from examples. Supplied curves illustrate underfitting and overfitting without requiring an untaught model family.
Python skill: Read a dictionary setting with get_params() and a learned attribute with a trailing underscore.
Meet the syntax
model.get_params()
['fit_intercept']
model.coef_[0]model.get_params()- Returns constructor settings chosen before fitting; these are not learned coefficients.
['fit_intercept']- Looks up the named setting in that dictionary.
model.coef_[0]- Reads the first learned coefficient. The trailing underscore identifies fitted state.
Follow the code
Use the numbered comments to connect each Python block to the workflow above.
fit_intercept = model.get_params()['fit_intercept']
distance_coefficient = float(model.coef_[0])
This practice: Read and run the Python. Next: Change · Settings, learned values and fit quality.
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.linear_model import LinearRegression
model = LinearRegression().fit(df[['distance']], df['duration'])Your task · Follow
From the supplied fitted line, store the fit_intercept setting in fit_intercept and the learned distance coefficient in distance_coefficient.
Hint 1 — Think
Read a dictionary setting with get_params() and a learned attribute with a trailing underscore.
Hint 2 — Tools
Use model.get_params(), ['fit_intercept'], model.coef_[0]. Read the visible syntax meanings before editing.
Hint 3 — Approach
From the supplied fitted line, store the fit_intercept setting in fit_intercept and the learned distance coefficient in distance_coefficient. Keep the supplied row order and inspect the named output after running.
Explained solution
fit_intercept = model.get_params()['fit_intercept']
distance_coefficient = float(model.coef_[0])
fit_intercept is a setting, while coef_ is estimated from training examples. Choosing a setting, learning coefficients and evaluating fit quality are separate actions. A fitted attribute proves a fit happened, not that predictions generalise.
Helpful prior knowledge: Read cross-validation evidence 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.