garbage ingarbage outthat' what we know from real life throughout this bookwe have seen that this pattern also holds true when applying machine learning methods to training data looking backwe realize that the most interesting machine learning challenges always involved some sort of feature engineeringwhere we tried to use our insight into the problem to carefully craft additional features that the machine learner hopefully picks up in this we will go in the opposite direction with dimensionality reduction involving cutting away features that are irrelevant or redundant removing features might seem counter-intuitive at first thoughtas more information is always better than less information shouldn' the unnecessary features be ignored after allfor exampleby setting their weights to inside the machine learning algorithm the following are several good reasons that are still in practice for trimming down the dimensions as much as possiblesuperfluous features can irritate or mislead the learner this is not the case with all machine learning methods (for examplesupport vector machines love high-dimensional spacesbut most of the models feel safer with less dimensions another argument against high-dimensional feature spaces is that more features mean more parameters to tune and higher risk of overfitting the data we retrieved to solve our task might just have artificial high dimensionswhereas the real dimension might be small less dimensions mean faster training and more variations to try outresulting in better end results if we want to visualize the datawe are restricted to two or three dimensions this is known as visualization sohere we will show you how to get rid of the garbage within our data while keeping the valuable part of it