Logistic Regression vs Decision Tree

Logistic Regression is primarily used for binary classification problems, providing probabilities of class membership based on input features. It's simple, interpretable, and works well with linearly separable data.
Decision Trees are versatile, capable of handling both classification and regression tasks. They recursively split the data based on features to create decision rules. They're prone to overfitting but can be regularized.