Editing Supervised learning (section)

===Dimensionality of the input space===

A third issue is the dimensionality of the input space. If the input feature vectors have large dimensions, learning the function can be difficult even if the true function only depends on a small number of those features. This is because the many "extra" dimensions can confuse the learning algorithm and cause it to have high variance. Hence, input data of large dimensions typically requires tuning the classifier to have low variance and high bias. In practice, if the engineer can manually remove irrelevant features from the input data, it will likely improve the accuracy of the learned function. In addition, there are many algorithms for [[feature selection]] that seek to identify the relevant features and discard the irrelevant ones. This is an instance of the more general strategy of [[dimensionality reduction]], which seeks to map the input data into a lower-dimensional space prior to running the supervised learning algorithm.