Part V Class Imbalance, People=pleasing, and Rigidity скачать в хорошем качестве

Part V Class Imbalance, People=pleasing, and Rigidity

In machine learning, class imbalance appears when one category dominates the dataset. The model learns the majority so well… that it stops recognising the minority at all. Human behaviour has an almost identical failure mode. When we over-adapt to others (people-pleasing), we predict everyone else’s needs and stop detecting our own. When we become rigid, we over-protect ourselves and stop recognising others. Both look different. Both come from the same error: a badly balanced training set. Your nervous system is constantly training a behavioural model: • approval becomes the “majority class” → you abandon boundaries • self-protection becomes the “majority class” → you abandon connection The goal is not choosing one side. The goal is learning to correct the bias. In ML we solve class imbalance by: • resampling (giving a voice to neglected signals) • weighting errors (some mistakes matter more than others) • new metrics beyond accuracy (being “liked” isn’t the same as being understood) In life, the equivalent becomes: • boundaries • empathy • discernment Because a model that predicts only one class looks accurate — but it has learned nothing. And a person who only pleases or only resists isn’t balanced — they’re biased by experience. Maturity is not hardness and not softness. It is calibrated responsiveness. #MachineLearning #ClassImbalance #Psychology #PeoplePleasing #Boundaries #EmotionalIntelligence #AIeducation #PersonalGrowth #SelfAwareness #DecisionMaking #MentalModels #Prompt2Self