02 · ML PIPELINE
Build carefully.
Evaluate honestly.
A streaming sparse logistic model evaluated against meaningful baselines, with accuracy placed in its proper class-imbalance context.
MODEL APPROACH
Candidate clicked
vs. not clicked.
loss = log_loss · trained in streaming sparse batches · 249 encoded final features
BASELINES → FINAL MODEL
Average Precision comparison
Scale: 0.00 to 0.11 Average Precision. The bars preserve the numerical ordering and relative magnitude of the three results.
FINAL EVALUATION
RANKING
PLAIN-LANGUAGE EXPLANATION
Better concentration, not a guaranteed click.
The development set contains only about 4.06% positive engagement. A no-skill model therefore begins near 0.0406 Average Precision. The final model reaches 0.1065, meaning positive-engagement candidates are concentrated more effectively toward higher model scores than under chance or the simple affinity baseline.
IMPORTANT CLASS-IMBALANCE LESSON
95.94% accuracy
can still miss nearly everything.
This is why accuracy is not the primary metric.
GOVERNANCE
A click is an engagement signal only. It is not evidence of helpfulness, satisfaction, spiritual benefit, causal effect, or Strongr Daily production effectiveness.