The correct option is A. High bias.
A model that exhibits both high training error and high test error is underfitting the data. This means the algorithm is too simple or constrained to capture the underlying patterns within the animal images.
In the context of machine learning model evaluation:
High Bias means the model makes strong, incorrect assumptions about the dataset (such as trying to fit a linear boundary to highly complex, non-linear image data). This prevents the model from learning the relationships even within the data it was trained on, directly resulting in a high training error.
High Variance would be observed if the model overfit the data—characterized by a low training error but a high test error, meaning it memorized the training noise instead of generalizing.
Because Peanut's model fails to perform well even on its own training set, the most confident and direct conclusion is that it suffers from high bias.
