What does variance indicate in the context of an AI model?

Variance refers to the model's sensitivity to the fluctuations in the training dataset. In machine learning, a model with high variance pays too much attention to the training data, including its noise and outliers. This sensitivity leads to a model that can perform well on the training data but poorly on unseen or new data, as it has essentially "memorized" the training examples rather than capturing the underlying patterns.

Understanding variance is crucial for model building because it helps in diagnosing the risk of overfitting, which occurs when a model is too complex and captures noise rather than the intended signal in the data. Thus, by recognizing that variance indicates the level of sensitivity to changes in the training dataset, practitioners can make informed decisions about model complexity and regularization techniques to improve generalizability.