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2 cards from this section. Quick, good the night before a test.
What does bias mean in the context of model evaluation?
Think about incorrect interpretations.
What happens when a model has high bias?
Consider how a model misses patterns.
What is considered a major impact of high bias in a model?
Think about how underfitting differs from capturing data patterns.
True or False: Bias in machine learning always indicates a well-performing model.
Consider what underfitting means for a model's effectiveness.
Identify a real-life scenario where high bias might occur in a machine learning application. Describe the implications for stakeholders involved.
Think about how oversimplification affects decision-making.
Suppose you are tasked with improving a model suffering from high bias. What steps would you take to adjust its configuration or data inputs?
Reflect on the elements in ml models that can benefit from improvement.
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