ML & Data Engine
How we process data to generate fit predictions
Data Sources
Behavioral Data
From scenarios and avatar
Psychometric Data
Big Five, RIASEC, learning style
Lifestyle Data
Social preferences, environment needs
Data Architecture
Our architecture processes multiple data streams, normalizes them, and feeds them into our ML Fit Intelligence Engine to generate comprehensive fit profiles.
Fit Prediction Methodology
We use ensemble machine learning models trained on validated outcomes to predict fit across six dimensions. Models are continuously improved through ThriveTrack validation.
Validation Approach
Every 90 days, we validate predictions against real student outcomes, refining our models to improve accuracy. This continuous feedback loop ensures our predictions get smarter over time.
Explainability
Every fit score includes rationale explaining why a college is a match. We believe in transparent, explainable AI that students and counselors can understand and trust.