What are the advantages of using a neural network over generating classification trees or using other machine learning methods? I'm not too familiar with how neural nets work, but it seems like they require more creator input than other methods, which could be good or bad I suppose.
Neural networks, and the plain old trusted logistic regression :) handles raw, continuous data better than the other learning algorithms. For example, if your inputs are images or audio recordings, it's really hard to do classification with decision trees or random forests as you'd need to construct the features manually. What would be a feature: color densities, color histograms, edges, corners, Haar-like, etc.? The promise of multilayer neural network is that given a lot of data, the right network structures, an appropriate learning strategy, and a huge farm of GPUs, the network can automatically learn the right features from raw data in the first layers, and utilizes the features in later layers. The big advantage of this approach is that you abstract away the domain problems (hopefully), and focus on picking the right network design, the right learning strategy, collecting a good data set etc. Neural network training is also easy to parallelize, so Google and the like can leverage their huge infrastructures.
Now if the features in the domain problem is more well defined, like credit ratings, and data is sparse, and domain expertise is available, decision trees are perfectly valid options.
