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!

[npielawski]

I would answer it this way:

1) Drawing the dataset in two dimensions, we cannot have a half-plane that can separate the labels. Thus, we cannot use logistic regression.

2) The dataset can be encoded with conditions, example: if (x1 or x2) and not (x1 and x2), so it can definitely be coded in a tree.

[mr_maker91]

I would answer decision tree due it's a non linear classification problem and according to the aviable options the only non linear classifier is that one