I am solving a binary classification problem with 4 predictor variables. The variables didn't seem to be linearly separable. I have used Neural Networks and Kernel SVM which work and give desired accuracy , but in turn are too complex to interpret and have latency issues.

Are there any transformation like Box-Cox or power transform that I can apply on the data and then use logistic regression/ decision tree for classification. I can sacrifice few % of accuracy to get a simple interpretable model.

What different methods can be used for data transformation?