A company is developing an ML model to predict customer churn. The model performs well on the training dataset but does not accurately predict churn for new data.
Which solution will resolve this issue?
A.
Decrease the regularization parameter to increase model complexity.
B.
Increase the regularization parameter to decrease model complexity.
The most effective solution to resolve overfitting and improve the model’s performance on new data is B. Increase the regularization parameter. This helps make the model simpler, reducing the likelihood of overfitting and improving its ability to generalize.
Increase the regularization parameter to decrease model complexity.
Increasing the regularization parameter helps prevent overfitting by penalizing more complex models, encouraging the model to generalize better to new data.
Would you like more detailed information on how to implement this change or any other aspect of model tuning?
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