Questions

1. You are working as a lead data scientist for a retail company. Your team is building a regression model and using the linear learner built-in algorithm to predict the optimal price of a particular product. The model is clearly overfitting to the training data and you suspect that this is due to the excessive number of variables being used. Which of the following approaches would best suit a solution that addresses your suspicion?

   a) Implementing a cross-validation process to reduce overfitting during the training process.

   b) Applying L1 regularization and changing the wd hyperparameter of the linear learner algorithm.

   c) Applying L2 regularization and changing the wd hyperparameter of the linear learner algorithm.

   d) Applying L1 and L2 regularization.

   Answers

   C, This question prompts about to the problem of overfitting due an excessive number of features being used. L2 regularization, which is available in linear learner through the wd hyperparameter, will work as a feature...