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Coding workflows · Classification confusion matrices
03
A concrete starting point
Build a confusion matrix from expected and predicted labels. Reject length mismatches and unknown labels, keeping zero counts for classes never observed.
Define the change
Describe the input, expected output and boundaries that must stay intact.
Inspect the work
Review the proposed edits and any commands before relying on their result.
Check the outcome
Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
Classification confusion matrices
A concrete starting point
def confusion(expected, predicted, labels):
if len(expected) != len(predicted) or len(set(labels)) != len(labels):
raise ValueError("labels or lengths")
index = {label: i for i, label in enumerate(labels)}
matrix = [[0] * len(labels) for _ in labels]
for actual, guess in zip(expected, predicted):
if actual not in index or guess not in index:
raise ValueError("unknown label")
matrix[index[actual]][index[guess]] += 1
return matrixThe example is a starting point for a real project, not a claim that generated code is already tested. Adjust paths, dependencies and expected behavior to match your application.
