What is the best machine learning algorithm for large, noisy datasets with interaction between variables? My initial thought was a neural network but I don't see how a neural network can properly predict interaction between variables (ie. x1 * x2) since each node is just a sum of previous inputs?
Would a decision tree be better suited at capturing the interaction between variables?
My dataset is large, with 400 features and 5,000,000 instances. All data is in percentile and the label is also a percentile. The dataset is quite noisy as well, (customer data, predicting likelihood of becoming a return customer).
Probabilistic Random Forest tends to work better then other algorithms on noisy datasets. But the data you are using also plays a major role on whether a algorithm will work or not.
Check this paper Probabilistic Random Forest for more details. Happy Learning