I am trying to read the documentation of Vowpal Wabbit and it doesn't specify how to select specific learning algorithms (Not loss) like SVM,NN, Decision trees, etc. How does one select a specific learning algorithm?
Or does it select the algorithm itself depending on problem type (regression/classification like an automl type or low-code ML library?
There are some blogs showing to use Neural networks with -nn command but that isn't part of documentation--is this because it doesn't focus on specific algorithm, as noted above? If so, What is Vowpal Wabbit in essence?
Vowpal Wabbit is based on online learning (SGD-like updates, but there is also --bfgs if you really need batch optimization) and (machine learning) reductions. See some of the tutorials or papers to understand the idea of reductions. Many VW papers are also about Contextual Bandit, which is implemented as a reduction to a cost-sensitive one-against-all (OAA) classification (which is further reduced to regression). See a simple intro into reductions or a simple example how binary classification is reduced into regression.
As far as I know, VowpalWabbit does not support Decision trees nor ensembles, but see --boosting and --bootstrap. It does not support SVM, but see --loss_function hinge (hinge loss is one of the two key concepts of SVM) and --ksvm. It does not support NN, but --nn (and related options) provides a very limited support simulating a single hidden layer (feed-forward with tanh activation function), which can be added into the reduction stack.