Build the first working ThresholdModel class

Build only what the claim requires: strict and loose instances return different Booleans for the same score list. 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.

class ThresholdModel:
    def __init__(self, name, threshold):
        self.name=name; self.threshold=threshold
    def predict(self, scores):
        return [s>=self.threshold for s in scores]
strict, loose = ThresholdModel('Strict',0.8), ThresholdModel('Loose',0.4)
print(strict.predict([0.5,0.9]), loose.predict([0.5,0.9]))

Expected evidence: (False, True) then (True, True). 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 Strict(threshold=0.8), Loose(threshold=0.4), scores=(0.5,0.9). 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 ThresholdModel class, print or log at least three intermediates that map to the claim (strict and loose instances return different Booleans for the same score list). Good intermediates are values a teammate could recompute with a calculator or diff. Bad intermediates are framework traces you cannot explain.

Re-run with Strict(threshold=0.8), Loose(threshold=0.4), scores=[0.5,0.9] 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 reuse one predict method across two named models with different thresholds. 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 python-classes, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about Strict(threshold=0.8), Loose(threshold=0.4), scores=(0.5,0.9) 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: two handwritten prediction tables before coding the class.

For this page specifically, success looks like a deterministic path with printed intermediates while still centering the user decision to reuse one predict method across two named models with different thresholds. If you cannot point to a file, command, or assertion that proves that for the ThresholdModel class, stay on this page instead of advancing.

Object-oriented programming glossary

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