Outlier detection is a research area within the field of data mining and machine learning that focuses on identifying and removing anomalies or outliers in a dataset. An outlier is a data point that significantly deviates from the rest of the data and may indicate errors, fraud, or interesting patterns in the data. Outlier detection techniques aim to separate these unusual data points from the rest of the dataset in order to improve the accuracy and reliability of data analysis and predictive models. Outlier detection methods can be unsupervised, semi-supervised, or supervised, and they range from simple statistical approaches to more complex machine learning algorithms.