Loss Functions: Cross-Entropy, MSE & Focal Loss
Mathematical Objectives for Optimization, Probability Calibration & Severe Class Imbalance
#Loss Functions#Optimization#Cross-Entropy#Focal Loss#Class Imbalance
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STAGE 1 / 7— Anti-Pattern
Section 1: The LLM Anti-Pattern vs Right-Sized Model
The Naive Generative LLM Approach:
Evaluating classification quality using raw qualitative prompt outputs without computing formal statistical loss metrics.
Why It Fails in Production:
Qualitative evaluation cannot quantify uncertainty, calibration, or loss divergence during production shifts.
Targeted Algorithm (Loss Functions: Cross-Entropy, MSE & Focal Loss)
Latency:0.001ms
Cost / 1M Ops:$0.00
Determinism:Exact Mathematical Objective
Generative LLM Alternative
Latency:N/A
Cost / 1M Ops:N/A
Determinism:Unscientific Eye-Test