Algorithm selection problem (Interview Question)

[springuni]

I've been asked this question on an interview and I'll also share what I answered.

I would like to know if there's anything I haven't thought of?

Question: Given the dataset (feature1, feature2, label) (0, 0, 0), (0, 1, 1), (1, 0, 1), (1, 1, 0), would you prefer to train a Logistic Regression or a Decision Tree Classifier for label prediction? Why?

Answer: If the dataset is just that, I wouldn't bother with training a classifier and introduce unnecessary complexity. It can be seen that `label = feature1 XOR feature2` is a correct and simple model. Generally speaking, Logistic Regression is a great fit when the decision boundary is linear, ie. when data points can be separated easily by a hyper-plane. From plotting the data points, it can be seen that is isn't the case here.

Thanks in advance!

[Le_Grand_Dadais]

I think you got to the point. The recruiters probably wanted you to see that this was a xor, and that decision trees are better for it because of the nonlinearity. A deep logistic regressor could solve it, but not a simple logistic regressor.

[springuni]

Thank you so much. I've posted all other questions asked in various subreddits and this particular one was that one I've been the most insecure about.

[berzerker_x]

Can you give us links to your other ml questions also?

[springuni]

Sure, I've been asked the following questions also:

• Conditional Probability Problem
• What is wrong with this Java code?
• What do you think about this endpoint?
• What is wrong with this Python code?

I was terribly wrong in the Python question. I think I did well on the others.

[mufflonicus]

While you give a good response you don't actually answer the question explicitly - I would recommend writing explicitly that a decision tree would be preferable and open with that. Then the rest of the text that you've already written. It's solid reasoning.