1 link tagged with all of: classification + decision models + calibration + evals
Click any tag below to further narrow down your results
Links
The author compares newer decision models against LLM judges for automated evals on a leasing-agent failure task. The main takeaway is that the default 0.50 threshold often leaves performance on the table, and that the scores need tuning and calibration before you trust them.
- The default 0.50 decision threshold can miss nearly all failures; tuning it per model, using F1 with an 80% precision/recall floor, improves results.
- OpenAI Decisions had the best separation (F1 about 0.87) but cost about 4.5 times more than Perplexity Decider (F1 about 0.79) per 100 eval runs.
- Raw model probabilities are often poorly calibrated; Platt scaling helped Perplexity, Liquid d1, and Clef Flash, but made OpenAI's slightly worse.