4 min read
Debug the data before the model
When an ML experiment behaves strangely, the fastest route forward is often a tiny, slightly boring inspection of the inputs.
Machine LearningDebugging
A model can fail in sophisticated ways, but many experiments fail for ordinary reasons: labels are shifted, units changed halfway through a dataset, or a preprocessing step quietly flattened the signal we cared about.
My first-pass checklist
- Print shapes, ranges, missing values, and class counts.
- Visualize a few raw examples before and after preprocessing.
- Try to overfit a deliberately tiny batch.
- Compare against a baseline that is almost embarrassingly simple.
python
print(x.shape, y.shape)
print(x.min(), x.max())
print(np.unique(y, return_counts=True))None of this is glamorous. That is exactly why it works: it removes clever explanations until the pipeline has earned them.