I have one dependent binary categorical variable, and one independent continuous variable. There is a lot of randomness deciding the result of the dependent variable.
The relationship between the independent variable and the dependent variable is linear. I have 2,000 data points to train data on. Some possibilities are:
- Logistic regression - simplest option
- SVM (support vector machines)
- Naive bayes
- Random forests - I see this does well on kaggle, but I have a simple one variable linear relationship, so it seems random trees isn't necessary here.