For most prediction and classification problems a gradient-boosted tree beats a neural network on accuracy, cost and explainability at once. We pick the technique the problem calls for, keep the pipeline reproducible, and stay able to explain a prediction — which matters when someone is refused credit or flagged for review.
Artificial Intelligence & Generative AI
Machine Learning
Classical models, which are still the right answer more often than not.
Overview
What the engagement covers
- Framing the problem and establishing the baseline to beat
- Feature engineering and leakage checks that are taken seriously
- Model selection favouring explainability where accuracy is comparable
- Validation designed against the way the model will really be used
- Fairness testing where decisions affect people
What you leave with
- Accuracy measured against the baseline, not in the abstract
- Predictions that can be explained to the person affected
- A pipeline that reproduces its own results
Artificial Intelligence & Generative AI
The rest of this practice
Machine Learning, scoped to your estate
Tell us where you are and what it has to be worth. We will come back with a scope, a sequence and a number.