PDE Question 51

Community Discussion

Written by other learners. Comments can disagree with the marked answer — check the official documentation when in doubt.

• Chose ACE1

  A. Because getting more training samples reduces significantly the risk of overfitting since the algorithm can learn from a more general dataset. C. Introducing lots of features increases the risk to introducing irrelevant information, driving the model to avoid focusing on the truly important patterns. E. Because regularization increases the penalty term to the loss function, which discourages complex models with large coefficients avoiding overfitting.

• Chose ACE5

  To address the problem of overfitting in training a spam classifier, you should consider the following three actions: A. Get more training examples: Why: More training examples can help the model generalize better to unseen data. A larger dataset typically reduces the chance of overfitting, as the model has more varied examples to learn from. C. Use a smaller set of features: Why: Reducing the number of features can help prevent the model from learning noise in the data. Overfitting often occurs when the model is too complex for the amount of data available, and having too many features can contribute to this complexity. E. Increase the regularization parameters: Why: Regularization techniques (like L1 or L2 regularization) add a penalty to the model for complexity. Increasing the regularization parameter will strengthen this penalty, encouraging the model to be simpler and thus reducing overfitting.

• Chose ACE2

  100% ACE We need more data because less data induces overfitting. We need less features to make the problem simpler to learn and not promote learning a very complex function for thousands of features that might not apply to the test data. We also need to use regularization to keep the weights constrained.