Logistic regression vs naive bayes and random forest I have a dataset that is a high dimensional imbalanced dataset. The dataset is a categorical data set and I applied label encoder to transfer categorical values into numerical values. the dataset is a tabular dataset. I also use the mean imputation method to impute missing values. I use the oversampling technique on the training set and got the prediction recall around 0.800 for logistic regression. I used other classifiers like Naive bayes, random forest but did not get such a high prediction accuracy.
I used weka software for data training.
My question is why I got good accuracy for logistic regression and not for other classifiers?
Thank you.

This is speculation, since your comments mention that you do not have access to the training performance, but with poor out-of-sample performance by fancy models, this sounds like a classic case of overfitting.
Yes, a model like a random forest allows much more flexibility in the modeling than a vanilla logistic regression, but this runs the risk of fitting the model to coincidences in your data (noise) instead of genuine trends (signal). In the extreme, think about playing connect-the-dots.
When you go and apply this overfitted model to new data, the predictions have little to do with the true trend, and your predictive accuracy is poor.
Balancing the ability to fit complicated trends while guarding against overfitting is really the key to doing good machine learning work.
