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Machine-learning data splits
Model evaluation starts with the data boundary. Model Ledger helps you examine split logic and preprocessing code before drawing conclusions from a result.
● ● ●Model Ledger●
A concrete starting point · Machine-learning data splits ×
train_groups, test_groups = split_groups(group_ids)
assert not set(train_groups) & set(test_groups)
train = rows_in_groups(data, train_groups)
test = rows_in_groups(data, test_groups)✳Split machine-learning data by the entity that could leak across sets.
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Keep each group on one side and fit preprocessing on training data before applying it to held-out rows.
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Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.
A concrete starting point
train_groups, test_groups = split_groups(group_ids)
assert not set(train_groups) & set(test_groups)
train = rows_in_groups(data, train_groups)
test = rows_in_groups(data, test_groups)