Build the first working tiny linear model

Build only what the claim requires: fitted slope/intercept, one held-out prediction, and MAE are all printed. Prefer boring, deterministic code over frameworks you cannot yet explain. Run the path twice; identical output on this fixture is a feature, not a lack of creativity.

import numpy as np
X=np.array([1.0,2,3,4]); y=np.array([2.0,4,5,7])
slope, intercept = np.polyfit(X,y,1)
pred=slope*5+intercept
mae=abs(pred-8)
print({'slope':float(slope),'intercept':float(intercept),'holdout_pred':float(pred),'mae':float(mae)})

Expected evidence: slope, intercept, holdout pred, mae. Read each printed intermediate as part of the argument that the path works—not as decoration.

Narrate the journey from raw input to result for a single example from small hours/score table with one held-out pair. If you cannot name an intermediate, the implementation is still too opaque for this lab. Only after this path is solid should you generalize data sources or UI.

While implementing the tiny linear model, print or log at least three intermediates that map to the claim (fitted slope/intercept, one held-out prediction, and MAE are all printed). Good intermediates are values a teammate could recompute with a calculator or diff. Bad intermediates are framework traces you cannot explain.

Re-run with small hours/score table with one held-out pair twice. If the second run differs, either the path is nondeterministic (document the seed) or you have hidden global state—both are lab bugs until named.

Stop adding features once the path supports estimate slope/intercept from study hours and score a held-out row honestly. Extra UI, extra tools, or extra models belong in later chapters. The mastery bar for this page is simply: a deterministic end-to-end path with intermediates.

At the implementation stage for train-a-tiny-model, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about small hours/score table with one held-out pair that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: predict mean train score for the holdout before fitting.

For this page specifically, success looks like a deterministic path with printed intermediates while still centering the user decision to estimate slope/intercept from study hours and score a held-out row honestly. If you cannot point to a file, command, or assertion that proves that for the tiny linear model, stay on this page instead of advancing.

Overfitting glossary

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