IT & Computing
Machine Learning
A model is the easy part. The evaluation is what is marked.
A notebook that ends with "accuracy: 0.94" and no baseline, no class balance and no error analysis is a weak submission however good the number looks. Marks live in the evaluation: what you compared against, which metric suits an imbalanced problem, and where the model fails. We write that part properly.
What you get
- A dataset that is legally usable and documented
- Preprocessing steps recorded and justified
- A baseline before anything clever
- Metrics chosen for the problem, not for the score
- Error analysis instead of a single accuracy number
Related reading
Referencing
APA 7th Edition
The details that cost marks: capitals, italics, ampersands and DOIs.
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Thesis & Dissertation
Discussion Chapter
Where your results stop being numbers and become findings.
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