# Model Performance and Size of Data Set

Suppose we have a data set with $$4,000$$ labeled examples. The outcome variable is trinary (three possible categorical values). Suppose the accuracy of a given model is "bad" (e.g. less than $$50 \%$$).

Question. Should you try different traditional machine learning models (e.g. multinomial logistic regression, random forests, xgboost, etc.), get more data, or try various deep learning models like convolutional neural networks or reccurent neural networks?

If the purpose is to minimize time and effort in collecting training data, would deep learning models be a viable option over traditional machine learning models in this case?