We will often have a large number of features to choose from, but we wish to select only a small subset. There are many possible reasons for this:
Reducing complexity: Many data mining algorithms need more time and resources with increase in the number of features. Reducing the number of features is a great way to make an algorithm run faster or with fewer resources.
Reducing noise: Adding extra features doesn't always lead to better performance. Extra features may confuse the algorithm, finding correlations and patterns that don’t have meaning (this is common in smaller datasets). Choosing only the appropriate features is a good way to reduce the chance of random correlations that have no real meaning.
Creating readable models: While many data mining algorithms will happily compute an answer for models with thousands of features, the results may be difficult to interpret for a human. In these cases, it may be worth using fewer features and creating a model that a human...