When we deal with data sets, high dimensions (that is, the number of features) may make the analysis extremely complicated. Dimension reduction is like "slimming" data, removing irrelevant or redundant "fat" and making high-dimensional data "slimming" and easy to handle. This paper will introduce the benefits and practical methods of dimensionality reduction to help readers better understand and apply this powerful tool.

There are many benefits

Dimension reduction can make the data easier to understand and explain, and can also prevent the model from over-fitting, and improve the calculation speed and storage efficiency. It is simply our right-hand man for data analysis and modeling!

Various practical methods

Principal component analysis, factor analysis and linear discriminant analysis are all practical dimensionality reduction methods. Principal component analysis can turn high-dimensional data into low-dimensional data, while retaining the most important information, which is the "Swiss army knife" of data.

Pay attention to information loss

Dimension reduction may also lead to some information loss. In operation, we must balance the relationship between retaining effective information and maintaining the accuracy of the model.