Bài kiểm tra trang bị quá mức và thiếu trang bị

Câu trả lời và giải thích xuất hiện khi bạn làm bài kiểm tra.

1. Under comparable train and held-out conditions, an illustrative model scores 98% on training and 71% on held-out data. Which fitting concern is consistent with that gap?
   • Underfitting because training performance is necessarily poor
   • A guarantee that the held-out set has no information
   • Overfitting to training-specific patterns
   • Proof that every larger model will fix the gap
2. Which performance pattern can be consistent with a model underfitting useful structure?
   • Poor performance on both its training and held-out data
   • Excellent training performance with poor held-out performance only
   • Guaranteed perfect performance on both datasets
   • A guarantee that the objective is minimized globally
3. In a bias-variance analysis, what does high estimator variance concern?
   • An unavoidable lack of any measurement noise
   • Sensitivity of fitted predictions to which training sample is used
   • A guarantee that the model is a linear function
   • The wall-clock time of a training epoch
4. A house-price model has too limited a representation of relevant inputs and fits poorly. Which candidate change should be evaluated?
   • Removing all relevant inputs without evaluation
   • Assuming a bigger model must always be correct
   • Using the final test set repeatedly as training data
   • Adding useful features or comparing a more suitable model representation
5. Tại sao bộ kiểm tra hoặc xác thực được giữ lại lại cần thiết để phát hiện trang bị quá mức?
   • Nó đo lường hiệu suất trên dữ liệu mà mô hình không học được từ đó, cho thấy khoảng cách khái quát hóa
   • Nó làm cho quá trình đào tạo diễn ra nhanh hơn
   • Nó đảm bảo mô hình sẽ không bao giờ mắc lỗi
   • Nó thay thế sự cần thiết của một hàm mất mát
6. What pattern is called model-size double descent in the studied examples?
   • Every dataset must produce exactly the same error curve
   • Training loss is halved exactly twice on every epoch
   • Test error can rise near an interpolation regime and then fall as capacity increases
   • Two models are always trained and averaged
7. Why should the final test set be kept separate from repeated model-selection choices?
   • It must be used for every learning-rate decision
   • It guarantees zero future prediction error
   • It removes the need for validation data
   • Repeated selection on it can make the final reported estimate optimistic
8. In a squared-error bias-variance decomposition with fixed information and observation conditions, what can model-capacity changes alone fail to remove?
   • The definition of the model title
   • The conditional noise component
   • Every possible systematic bias by definition
   • The requirement to calculate predictions