A machine learning model performed well during experiments, but immediately failed to generalize when placed in a production environment. What should a Machine Learning Specialist do to address this…

While dimensionality reduction can sometimes help reduce complexity and overfitting, it is not the primary or most direct method to fix an already overfit model, as regularization specifically targets the model's learning process to prevent memorization.

Adding additional features typically increases model complexity and the risk of overfitting, which would worsen the problem of poor generalization in production rather than solving it.

Adding regularization techniques, such as L1, L2 (weight decay), or dropout, directly addresses overfitting by penalizing complex models and encouraging simpler, more generalizable solutions, thereby improving the model's performance on unseen data in a production environment. These techniques prevent the model from memorizing the training data, forcing it to learn more robust features.

Modifying the learning rate primarily affects the convergence speed and stability of the training process, but it does not directly prevent a model from overfitting to the training data once convergence is achieved.