There is a dataset with about 20 dimensional input vectors and binary output vector. There are about 100K training examples. When trained using logistic regression the accuracy on training set is about 50% and trained using random forest classifier(100 trees) the accuracy on training set is 100%. It is a general notion that random forest classifiers are less prone to overfitting. But in this particular case, the test accuracy of the logistic regression classifier is much higher than the random forest classifier. What can we infer about the distribution of the dataset from this observation?