30.4.2. Model Building

Try these first

1. 1.

   What is the purpose of model building in machine learning?

   Hint

   Think about the learning and prediction aspects.

2. 2.

   What is cross-validation used for?

   Hint

   It ensures the model performs well on unseen data.

3. 3.

   Why is selecting the right algorithm critical in model building?

   • It determines data cleaning methods
   • It impacts model performance
   • It has no effect

   Hint

   Think about the performance outcome of different algorithms.

4. 4.

   True or False: Hyperparameters are set after model training.

   • True
   • False

   Hint

   Consider when adjustments take place in the workflow.

5. 5.

   You have a dataset with both labeled and unlabeled data. How would you approach model building in this scenario, considering the advantages of using unsupervised learning methods?

   Hint

   Think about the benefits of exploratory data analysis.

6. 6.

   Design a strategy for hyperparameter tuning using cross-validation for a complex neural network model aimed at image classification. Detail each step.

   Hint

   Consider how to systematically explore hyperparameter spaces.