Results for tag: "Outlier Detection"

Outlier Detection is a sub-field of Machine Learning and Data Mining that focuses on identifying data points that are significantly different from the rest of the data. Outlier Detection algorithms are used to detect anomalies in data sets, such as fraudulent transactions, rare events, or errors in data collection. Outlier Detection can be used to improve the accuracy of predictive models, detect fraud, and identify potential opportunities. The Outlier Detection market is growing rapidly, driven by the increasing demand for data-driven decision making and the need for more accurate predictive models. Companies in the Outlier Detection market include Anodot, DataRobot, H2O.ai, IBM, Microsoft, Oracle, and SAP. Show Less Read more