We have an input composed of several features. Why is it better to use many of them in a Machine Learning algorithm, instead of using only the better discriminant one? And in which way many features are combined in order to produce a good model?
In general, you should add features carefully and try to add features which are not correlated with each other. Without given data, no one can predict if the model will be really good on just one feature. We definitely don't want to overfit but that doesn't mean we underfit and don't use the features which provide us discriminatory information.
What do you mean by "And in which way many features are combined in order to produce a good model ?" Are you asking a feature selection method?