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Base URL, credentials and a reachable server.
Product
Product features · Classification confusion matrices
01
Reject length mismatches and unknown labels, keeping zero counts for classes never observed.
Selection
A model available through the configured endpoint.
Capabilities
Context limits and tool support are determined by the model and server.
Classification confusion matrices
● ● ●MatrixTern●
A concrete starting point · Classification confusion matrices ×
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 matrix✳Build a confusion matrix from expected and predicted labels.
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