It’s been a while since we last wrote about the latest changes in our command line tool, BigMLer. In the meantime, two unsupervised learning approaches have been added to the BigML toolset: Clusters and Anomaly Detectors. Clusters are useful to group together instances that are similar to each other and dissimilar to those in other groups, according to their features. Anomaly Detectors, on the contrary, try to reveal which instances are dissimilar from the global pattern. Clusters and anomaly detectors can be used in market segmentation or fraud detection respectively. Unlike trees and ensembles, they don’t need your training data to contain a field that you must previously label. Rather, they work from scratch which is why they’re called unsupervised models.
In this post, we’ll see how easily you can build a cluster from your data, and a forthcoming post will do the same for anomaly detectors. Using the command line tool
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