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Bias vs Variance Explained Simply 🤖 | Why AI Models Fail
Understanding bias and variance is crucial for improving AI models. Bias refers to overly simplistic assumptions, while variance reflects sensitivity to fluctuations in training data. Overfitting and underfitting are common issues related to these concepts.