What term describes the extent to which a model's predictions vary for different subsets of training data?

The term that describes the extent to which a model's predictions vary for different subsets of training data is variance. Variance refers to the model’s sensitivity to the fluctuations in the training data it learns from. When a model has high variance, it means that its predictions can change significantly when trained on different datasets, leading to overfitting where the model captures noise rather than the underlying data patterns.

This concept is pivotal in understanding how well a model will generalize to unseen data. A model with high variance is less robust, as it adapts too closely to the training dataset, making it perform poorly when faced with new, unseen data. Conversely, models with low variance remain stable across different datasets but may struggle with capturing all relevant patterns if they also exhibit high bias.

The other terms do not specifically capture this aspect of model behavior. Bias refers to error due to overly simplistic assumptions in the learning algorithm that can cause it to miss relevant relationships in the data. Accuracy measures the number of correct predictions made by the model but does not reflect how those predictions vary with changes in the training data. Reliability pertains more to the consistency of the model outputs rather than the variability of predictions across different training sets.