If the accuracy of my current model is low ($50 \%$) and we want to minimize time in collecting more data, should we try other models? 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 &quot;bad&quot; (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 recurrent 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?
To know if your model needs more training data, try to plot out "learning curves", that are based on increasing size of the training set.

Basically, you calculate training and validation accuracy metrics for 1, 2, 3, 4, 5, ..., m  training samples. Size of validation set may be constant over time. If the accuracy is still rising when your data set is fully used, then you need more training data.