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10. Finding Independent Features

Chapter 10. Finding Independent Features

Most of the chapters so far have focused primarily on supervised classifiers, except Chapter 3, which was about unsupervised techniques called clustering. This chapter will look at ways to extract the important underlying features from sets of data that are not labeled with specific outcomes. Like clustering, these methods do not seek to make predictions as much as they try to characterize the data and tell you interesting things about it.

You’ll recall from Chapter 3 that clustering assigns every row in a dataset to a group or point in a hierarchy—each item fits into exactly one group that represents the average of the members. Feature extraction is a more general form of this idea; it tries to find new data rows that can be used in combination to reconstruct rows of the original dataset. Rather than belonging to one cluster, each row is created from a combination of the features.


  

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