How do you assess the results of a logistic regression?
Which evaluation metrics you know? Something apart from accuracy?
Which is better: Too many false positives or too many false negatives?
What precision and recall are?
What is a ROC curve? Write pseudo-code to generate the data for such a curve.
What is AU ROC (AUC)?
Do you know about Concordance or Lift?
You have a marketing campaign and you want to send emails to users. You developed a model for predicting if a user will reply or not. How can you evaluate this model? Is there a chart you can use?
Is regression some type of supervised learning? Why?
Explain the tradeoff between bias and variance in a regression problem.
A learning algorithm with low bias and high variance may be suitable under what circumstances?
What is regression analysis?
What do coefficient estimates mean?
How do you measure fit of the model? What do R and D mean?
What are some possible problems with regression models? How do you avoid or compensate for them?
Name a few types of regression you are familiar with? What are the differences?
What are the downfalls of using too many or too few variables for performing regression?
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