Which of two logistic scorecards earns its extra predictors, judged on likelihood, information criteria and a holdout sample, gets settled in this GF582 Unit 9 model comparison exercise. Searches like "gf 582 unit 9 assignment example", "gf582 unit 9 sample" and "gf582 unit 9 example" land here.
What a finished GF582 Unit 9 model comparison exercise looks like
Five pages with a coefficient table for each model, a comparison table and a short approval simulation. Model A uses credit score per 50 points, debt-to-income per 10 points and the log of years in business; all three are strongly significant, and each 50 points of score multiplies the odds of default by 0.42. Model B adds revolving utilization, recent inquiries, a construction-industry flag and collateral loan-to-value. The comparison table sets them side by side: log-likelihoods of minus 558.94 and minus 552.82, a likelihood ratio of 12.25 on 4 degrees of freedom with a p-value of 0.016, AIC of 1,125.88 against 1,121.63, BIC of 1,149.90 against 1,169.68, and holdout AUC of 0.769 against 0.794. Approving the best 800 of 1,000 holdout applicants leaves 25 defaults under A and 21 under B.
How a GF582 Unit 9 example is structured
Measures are grouped by what they reward. In-sample tests come first: the likelihood ratio and AIC both favor B, though both reward fit with only a mild penalty. BIC's heavier penalty on 3,000 cases reverses the order, and the text explains why neither criterion is wrong. Out-of-sample evidence follows as the deciding group, because a scorecard exists to rank applicants it has not seen. B's holdout AUC is higher by 0.025, and at an 80 percent approval rate it lets four fewer defaults through, although 53 holdout defaults make that margin fragile. The coefficient table then explains the gain: utilization carries it, with z of 3.06, while the industry flag and loan-to-value add nothing detectable. B earns its complexity over A, yet a trimmed model with utilization alone has the lowest AIC of the three and is recommended for validation.
Three predictors, all pulling weight
Score, debt-to-income and time in business carry z values of minus 10.55, 3.56 and minus 6.88. The baseline is strong, so any added term has a real bar to clear.
Four additions, examined one by one
Utilization at z of 3.06 matters; inquiries at 1.63 are borderline; the construction flag and loan-to-value, near 0.15, contribute nothing the data can detect.
Criteria that disagree
AIC falls from 1,125.88 to 1,121.63 while BIC rises from 1,149.90 to 1,169.68. The exercise reads the split as a warning that half of B's extra terms are paying no rent.
Applicants the models never saw
On the 1,000 held back, AUC rises from 0.769 to 0.794, the Brier score edges down, and defaults among 800 approvals fall from 25 to 21, a margin the text calls suggestive.
The verdict and a third model
B beats A, but a model adding only utilization reaches an AIC of 1,118.26 and a holdout AUC of 0.787. The exercise recommends validating that version before adopting either.
Where marks go in GF582 Unit 9
Model comparisons in GF582 lose the most when a single measure decides, usually in-sample fit, so the model with more predictors wins because more predictors always fit the training data at least as well. Most sections expect a holdout or cross-validation result and a penalized criterion side by side, with the verdict explaining any disagreement between them. Treating AIC and BIC as interchangeable, or quoting one without its direction of preference, draws a deduction. A larger model accepted whole, with non-significant terms kept and never discussed, misses the complexity question the unit is named for. Small holdout samples presented as decisive overstate the evidence. Coefficients in a logistic model reported as changes in probability rather than in log odds, without conversion, cost the interpretation marks.
Get a GF582 Unit 9 example written to your instructions
Two candidate models, the data or output behind them, and the criteria named for Unit 9 make up the brief, along with the rubric. The custom comparison lines up fit, penalized criteria and holdout evidence and says which model earns its extra terms, returning in 24-48h with the first at no charge.
GF582 Unit 9 questions, answered
Why can AIC and BIC choose different models?
They penalize extra parameters differently. AIC adds a fixed penalty per parameter, while BIC's penalty grows with the logarithm of the sample size, so on large datasets BIC favors smaller models more strongly. Neither is wrong; they encode different goals, prediction for AIC and identifying a simpler true model for BIC. A comparison should report both and say which goal the decision serves.
Is a holdout sample better than a penalized criterion?
It tests the thing a scorecard is for, ranking cases it has not seen, so many sections treat it as the stronger evidence. Its weakness is size: a holdout with few defaults gives noisy AUC and error figures. Cross-validation reuses the data to reduce that noise. Reporting holdout results beside AIC or BIC, rather than instead of them, is the usual expectation.
Should non-significant predictors always be dropped?
Not automatically. A predictor may be kept for business or regulatory reasons, or because theory says it belongs, and dropping terms one at a time on p-values alone can mislead. What matters is that the decision is argued. Say why each term stays or goes, and check whether removing it changes the holdout performance the model is judged on.