I’m working on a classification problem. However, my training dataset is very small (just 800 items in training dataset) and each data item contains a small number of features (just 5 features). Firstly, I used Logistic Regression to create a model for this dataset. Unfortunately, prediction accuracy of my model was very bad. Next, I used Neural Network model, but could not see any progress.
I suspect, number of training data items and number of features in each item are not enough for training Logistic Regressing and Neural Network.
So my question is what are the good techniques for modeling small datasets?