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Click-Through Rate (CTR) prediction models estimate the probability of users clicking on items based on feature interactions, inherently following a discriminative paradigm. However, this paradigm is prone to embedding dimensional collapse and information redundancy due to limitations of vanilla feature embeddings.This motivates us to reformulate it into a generative paradigm to generate new feature embeddings. Unlike sequential recommendation, which naturally fits a generative "next-item prediction" paradigm, it's hard to formulate CTR models into this paradigm without explicit feature order.Therefore, we propose a novel Supervised Feature Generation framework for CTR models, shifting from the discriminative "feature interaction" paradigm to the generative "feature generation" paradigm.Specifically, we predict each feature embedding based on the concatenation of all feature embeddings.Besides, this paradigm naturally accommodates a supervised binary cross-entropy loss to indicate whether the sample is positive or negative.The framework can reformulate nearly every existing CTR model and bring significant performance lifts.Moreover, it produces less-collapsed and redundancy-reduced feature embeddings, thereby mitigating the inherent limitations of the discriminative paradigm.The code can be found at https://github.com/USTC-StarTeam/GE4Rec.