# Machine learning algorithms for tabular dataset

I have a dataset with 120 features and 5000 instances. The dataset is combination of categorical and numerical values. It is a tabular dataset. My problem is a binary classification problem. I trained my dataset with all classic classification algorithms like Naive Bayes, Bayesian net, SVM, MLP, Random forest, Logistic regression etc. I would like to know is there any algorithms available in the machine learning field which is new and not the classic one and can be implemented using a tabular dataset.

I heard about Convolution neural network, deep neural network etc but I believe they are used in image data not tabular data.

You can try Generalized Additive Models (GAM). It models the response variable, $$y$$, as a sum of functions of individual features $$f_i(x_i)$$: $$y = \sum\limits_if_i(x_i)$$. You don't need to provide $$f_i$$, the algorithm finds learns them from the data. By analyzing the $$f_i$$, you can see, which feature contributes significantly, and in which way. You can even fit the $$f_i$$ to simple analytic functions and get an analytic dependence of $$y$$ on $$x_i$$.