With every learning problem our input is going to be in the form of a vector. The feature vector, meaning the features of the data itself, can affect the learning algorithm greatly. If the input feature vectors are very large, which is called high-dimensionality, then learning can be more difficult even if you only need just a few of those features. Sometimes, the extra dimensions confuse your learning algorithm, which results in high variance. This, in turn, means that you will have to tune your algorithm to have lower variance and higher bias. It is sometimes easier, if applicable, to remove the extra features from your data, thus improving your learning function accuracy.

That being said, a popular technique known as dimensionality reduction is used by several machine learning algorithms. These algorithms will identify and remove irrelevant features.