Target before features.
The first rule is to define exactly what is predicted, at what timestamp, and with which information set. That prevents target leakage and keeps the model honest.
More
Сhoose
Engineered
Creativity
Type: Research workflow
Focus: Validation / Diagnostics
Role: Feature design, validation, reporting
01 / Overview
A project built to explain the system behind the surface.
A machine learning workflow focused on signal quality, validation and explainability. The page explains the process before showing outputs.
ML projects can look impressive while hiding weak validation. This case study makes model evaluation, data leakage control and feature reasoning visible.
Approach
The structure follows a clean modelling loop: define the target, audit the data, build features, split properly, evaluate with cross-validation and communicate uncertainty.
Outcome
A more honest ML presentation: less hype, more evidence, clearer model behaviour.
Chronology matters.
For time-dependent data, random splits are dangerous. Walk-forward validation tests whether the signal survives when the model only sees the past.
Error is a surface.
Accuracy alone is not enough. The page reads residuals by regime, feature sensitivity, probability calibration and failure clusters.
Models drift silently.
A production signal needs monitoring for input drift, target drift and decision drift. The output is a living system, not a one-time score.
02 / Build
What the page documents.
- Feature audit and target definition
- Train/test split and cross-validation logic
- Baseline model before complexity
- Metric dashboard for error, stability and interpretability
03 / Reading
Why it belongs inside ENS.
This case study is designed as an explanatory page: context first, then method, then outputs. It avoids hiding behind screenshots and makes the thinking visible.
The visual language stays close to the existing ENS site: dark accents, strong spacing, restrained motion and clear editorial hierarchy.
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