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4 cards from this section. Quick, good the night before a test.
Define bias in the context of machine learning.
Think about what happens when a model simplifies the reality.
What is overfitting?
Relate it to complexity of the model.
What does high bias in a model indicate?
Consider the consequences of oversimplifying a model.
True or False: A model with high variance can perform well on training data but poorly on testing data.
This reflects the concept of overfitting.
Develop a small machine learning model on a dataset of your choice. Discuss occurrences of bias and variance in your model's performance based on the results you get. What improvements would you suggest?
Look for training vs testing performance to spot signs of bias and variance.
Suppose you have two models, one showing high accuracy on training data but low on testing data (Model A), and another showing consistent performance across both sets (Model B). Describe the likely bias and variance characteristics of both models.
Consider how performance on different datasets reflects underlying model assumptions.
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