Trong học máy, để giảm thiểu hiện tượng overfitting, ta có thể thực hiện các biện pháp nào sau đây?
Trả lời:
Đáp án đúng: C
Overfitting occurs when a model learns the training data too well, resulting in good performance on the training data but poor performance on new data. To reduce overfitting, we can apply the following measures:
- **Add more training data:** The more data, the more opportunity the model has to learn more general features rather than just memorizing the existing training data.
- **Use regularization techniques:** Such as L1, L2 regularization, dropout, and early stopping.
- **Reduce model complexity:** By reducing the number of parameters, layers, or neurons in the neural network.
- **Use a large batch size:** Using a larger batch size can help reduce noise during training, which can help the model generalize better and reduce overfitting.
Analysis of the options:
- **A. Use a large batch size:** Correct. A large batch size tends to reduce overfitting compared to a small batch size.
- **B. Use a small learning rate:** A small learning rate mainly affects the learning speed and does not directly reduce overfitting. It can help the model converge better but does not address the root cause of overfitting.
- **C. Add more training data:** Correct. Adding more training data helps the model learn more general features and reduces overfitting.
- **D. Increase the number of epochs:** Increasing the number of epochs can increase overfitting if there are no other control measures, as the model will have more opportunities to memorize the training data.
Since both A and C are correct, but the question only asks for one answer. In this case, C is often considered one of the most basic and effective approaches to reducing overfitting. However, in the context of the options given, using a large batch size is also a valid method for minimizing overfitting. Therefore, both A and C can be considered correct.
However, based on experience and practical implementation, adding more training data (C) is often a more effective solution than just adjusting the batch size (A). Therefore, option C is somewhat "stronger" in solving overfitting.
Therefore, both A and C could be correct answers. However, C is the more appropriate choice.
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