Which of the following statements about supervised learning is true?

In supervised learning, the relationship between model flexibility, bias, and variance is crucial for understanding model performance. Option B is true because increased flexibility in a model allows it to fit the training data more closely, which typically results in a reduction of squared bias.

When a model is more flexible, it can capture complex patterns in the data, thus minimizing the error due to bias. Bias refers to the error introduced when a model is too simplistic to capture the underlying trends in the data. By increasing flexibility, you allow the model to be more complex, which can adapt better to the training data.

In contrast, the relationship between variance and squared bias is not direct, as stated in option A. Variance captures how much the predictions of the model would vary with different training data sets. Typically, when flexibility increases, variance increases while bias decreases. The balance between these two components is a central concept in the bias-variance tradeoff in model evaluation.

Option C is incorrect because while increased flexibility can lead to lower bias on the training set, it may not necessarily lead to lower test mean squared error (MSE). In fact, overly flexible models can overfit the training data, resulting in poor generalization to unseen data, which can actually increase test