MLA-C01 Question 47
Select 3You are building a machine learning model to predict customer churn for an e-commerce platform. After initial training, you notice that your model is overfitting. Which of the following techniques can you use to address this issue?
- A
Add L2 regularization to your model's loss function
- B
Increase the learning rate of your optimizer
- C
Collect more training data
- D
Reduce the number of features used in the model
- E
Increase the model's complexity by adding more layers
Show answer and explanation
Correct answers: A, C, D
Explanation
Overfitting occurs when a machine learning model performs well on the training data but poorly on unseen data. Techniques like L2 regularization, collecting more training data, and reducing the number of features help to reduce overfitting by simplifying the model or improving generalization. In contrast, increasing the learning rate or adding more layers can exacerbate the problem or destabilize training.
- A. Correct.
Adding L2 regularization penalizes large weights in the model, helping to reduce overfitting by simplifying the model's behavior.
- B. Incorrect.
Increasing the learning rate can destabilize training and lead to poor convergence, which will not address overfitting.
- C. Correct.
Collecting more training data can help the model generalize better and reduce overfitting by providing more diverse examples.
- D. Correct.
Reducing the number of features can simplify the model and reduce the risk of overfitting by focusing on the most relevant information.
- E. Incorrect.
Increasing the model's complexity by adding more layers is likely to increase overfitting, as the model becomes more capable of memorizing training data.