All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.
All our activities include solutions with explanations on how they work and why we chose them.
You are a data scientist working on a binary classification problem. You have tried two different models for the task. Model A uses a simple hypothesis space with a linear model, while Model B employs a more complex hypothesis space with a high-degree polynomial. After evaluating both models, you notice that Model B fits the training data almost perfectly, but its performance on new, unseen data is not as good. On the other hand, Model A generalizes better to unseen data.
Based on this scenario, which model is likely suffering from overfitting?