I have a regression problem, with a million rows or so, around 10-15 features. What should work better on that particular setting? Neural network or regular regressors?
This is more of question how to select the correct machine learning algorithm, I would refer you to the following blog Which machine learning algorithm should I use?
Regression Algorithms models the relationship between variables that is iteratively refined using a measure of error in the predictions. Most popular examples are:
- Ordinary Least Squares Regression (OLSR)
- Linear Regression
- Logistic Regression
- etc ...
On the other hand, Artificial Neural Networks models are inspired by the structure and/or function of biological neural networks. "Neural networks currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing." Neural Networks and Deep Learning/. Neural Networks are hard to train; thus my recommentation not to start with Neutral Network.