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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.
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    $\begingroup$ 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? $\endgroup$
    – Glen_b
    Jun 11, 2013 at 1:13

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

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