Consistency
Columns follow a predictable schema so users can build repeatable pipelines.
Start With Data
Begin with clean, regularized features that are ready for modeling.
This page expands the dataset section of the numer.ai-style homepage into a dedicated view for data shape, schema logic, and operational use.
Why this section matters
The original numer.ai site quickly establishes that the data is both high quality and immediately usable. That framing is preserved here and extended into a more explicit product explanation.
- Each row represents an entity at a point in time.
- Features are standardized for direct use in model training.
- Targets represent future behavior over a delayed scoring window.
- Era-based slicing supports robust time-aware evaluation.
| id | era | feature_0 | feature_1 | feature_2 | target |
|---|---|---|---|---|---|
| n2b2e3dd16 | era412 | 0.75 | 0.00 | 0.25 | 0.25 |
| n177021a57 | era412 | 1.00 | 0.25 | 0.75 | 0.75 |
| n7830fa4c0 | era413 | 0.25 | 1.00 | 0.00 | 0.00 |
| nc584a184c | era413 | 0.25 | 0.00 | 1.00 | 1.00 |
A simple tabular view anchors the story in something tangible before the user reaches the modeling workflow.
Data Principles
Make the dataset legible before asking anyone to trust the platform.
Time Awareness
Era-based grouping makes it clear that evaluation must respect temporal splits.
Operational Clarity
The data page removes ambiguity about what belongs to features, labels, and live input.
Fast Adoption
New users should be able to start modeling without spending a day reverse-engineering schema.