Which machine learning algorithm should I use?

Question

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.

• Glen_b: It's next to impossible that the binary response is actually linear in the independent variable, unless the IV is very restricted in range --- and if it were actually linear, why would you use logistic regression? Isn't that nonlinear? Lastly, you state that it's linear with apparent certainty. Where does that certainty arise?

Answer 1

You should try all of the models you listed, and cross-validate them. It's the name of the site!