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1.

What is the difference between classical regression and classification models?

Regression models provide labels such as ‘cherry’/’banana’ but classification models calculate continuous numbers

Classification models and linear regression models are two names for the same thing

Classification models provide labels such as ‘cherry’/’banana’ but regression models calculate continuous numbers

2.

How can we improve real-world model performance?

By adding features

Both adding and removing features can be useful, depending on the situation

By removing features

3.

What is one reason why logistic regression uses log-loss rather than a more intuitive cost function?

Log-loss is stricter about model errors, even if they're close to correct

It's the only way to calculate error for categorical labels

Log-loss is more permissive to model error if that error is close to correct

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