title: Generating Difficult Classification Data Sets using scikit-learn
body: <p>I am trying to generate a range of synthetic data sets using make_classification in scikit-learn, with varying sample sizes, prevalences (i.e., proportions of the positive class), and accuracies. Varying the sample size and prevalence is fairly straightforward, but I am having difficult generating any data sets that have less than 50% accuracy using logistic regression. Playing around with the number of informative columns, the number of clusters per class, and the flip_y parameter (which randomly flips the class of a given proportion of observations) seem to reduce the accuracy, but not as much as I would like. Is there a way to vary the parameters of make_classification in such a way to reduce this further (e.g., to 20%)?</p>
  
  <p>Thanks!</p>