Question: In supervised machine learning, data scientist often have the challenge of balancing between underfitting or overfitting their data model. They often have to adjust the training set to make better predictions. What is this balance called?

1. the under/over challenge
2. balance between clustering classification
3. bias-variance trade-off
4. the multiclass training set challenge

Answer: The correct answer of the above question is Option C:bias-variance trade-off