Typically DataRobot works with labeled data, using supervised learning methods for model building. With supervised learning, you specify a target (what you want to predict) and DataRobot builds models using the other features of your dataset to make that prediction.
DataRobot also supports unsupervised learning where no target is specified and the data is unlabeled. Instead of generating predictions as in supervised learning, unsupervised learning surfaces insights about patterns in your data, answering questions like "Are there anomalies in my data?" and "Are there natural clusters?"
These unsupervised learning strategies are described in the following sections:
|Anomaly detection||Use unsupervised learning to detect abnormalities in your dataset.|
|Clustering||Use unsupervised learning to group similar data and identify segments.|