We have seen, in previous chapters, that too many features can degrade the performance of our models. What is known as the curse of dimensionality may negatively impact an algorithm's accuracy, especially if there aren't enough training samples. Furthermore, it can also lead to more training time and higher computational requirements. Luckily, we have also learned how to regularize our linear models or limit the growth of our decision trees to combat the effect of feature abundance. Nevertheless, we may sometimes end up using models where regularization is not an option. Additionally, we may still need to get rid of some pointless features to reduce the algorithm's training time and computational needs. In these situations, feature selection...
Selecting the most useful features
"More data, such as paying attention to the eye colors of the people around when crossing the street, can make you miss the big truck."
– Nassim Nicholas Taleb
