Does Model Diversity Actually Matter in an ML Ensemble?
Why combining different models doesn't guarantee a better ensemble: correlated errors, complementarity, underfitting, and validation on the full system.
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Machine Learning and Human Learning: Surprisingly Similar Problems
Memorization, generalization, evaluation, errors, and redundant knowledge: what certain Machine Learning problems reveal about learning itself.
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When an ML Improvement Doesn't Survive Validation
Why an apparent ML gain can disappear under more rigorous validation: statistical uncertainty, selection bias, redundancy, and system-level impact.
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