THE RESULT
What the data shows
The largest computed metric is 0.5529 for Includes completed duration / Logistic regression; the smallest is 0.3158 for Pre-call / Prior baseline. Metric: average_precision (Average precision).
The duration variant is intentionally invalid for pre-call use. Any score increase illustrates an information-timing problem.
| model | accuracy | balanced accuracy | macro f1 | roc auc | average precision | brier | information | variant |
|---|---|---|---|---|---|---|---|---|
| Prior baseline | 0.6842 | 0.5 | 0.4062 | 0.5 | 0.3158 | 0.2779 | Pre-call | Pre-call / Prior baseline |
| Logistic regression | 0.6806 | 0.5272 | 0.4906 | 0.6632 | 0.4818 | 0.2353 | Pre-call | Pre-call / Logistic regression |
| Random forest | 0.683 | 0.5036 | 0.4214 | 0.674 | 0.4641 | 0.2365 | Pre-call | Pre-call / Random forest |
| Prior baseline | 0.6842 | 0.5 | 0.4062 | 0.5 | 0.3158 | 0.2779 | Includes completed duration | Includes completed duration / Prior baseline |
| Logistic regression | 0.7028 | 0.5696 | 0.5587 | 0.7501 | 0.5529 | 0.2235 | Includes completed duration | Includes completed duration / Logistic regression |
| Random forest | 0.6941 | 0.5267 | 0.4721 | 0.7649 | 0.5525 | 0.2253 | Includes completed duration | Includes completed duration / Random forest |
THE METHOD
From source to answer
Paired comparison with identical chronological test rows and duration included only in the diagnostic variant.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
EVALUATION
How the result was checked
First 80% of ordered observations train; final 20% test.
Fixed models; default decision threshold; no holdout tuning.
Target and input features
Target: subscribed
Inputs: contact, month, day, campaign, pdays, previous, poutcome
THE NEXT DECISION
What follows from the finding
Reject duration from any pre-call model even if the retrospective score rises.
Where the conclusion stops
Observational marketing records do not identify campaign uplift. Month and row order are not precise timestamps; repeated-client identifiers are unavailable. Models are research diagnostics, not financial eligibility or automated contact decisions.
Related studies may reuse observations or holdouts. These are historical analyses; associations and backtests do not demonstrate commercial impact. Further model tuning needs new, untouched evaluation data.