9.3.2. Algorithmic Challenges

Try these first

1. 1.

   What is overfitting?

   Hint

   Consider what happens when a model is too complex.

2. 2.

   Give an example of an issue with data quality.

   Hint

   Think about data being messy or incorrect.

3. 3.

   What is overfitting?

   • Learning noise
   • Ignoring training data
   • Generalizing too well

   Hint

   Think about how a model behaves on training versus unseen data.

4. 4.

   True or False: Underfitting happens when a model is too complex.

   • True
   • False

   Hint

   Think about what happens when a model cannot learn enough.

5. 5.

   Given a set of training data, how can you identify and address overfitting effectively?

   Hint

   Think about evaluating and refining your approach based on model performance.

6. 6.

   How could you improve the model's performance if it is experiencing underfitting?

   Hint

   Reflect on how complexity influences learning capability.