马氏距离

资 源 简 介

马氏距离剔除异常样本，只要改下里面的数据就行了

详 情 说 明

The Mahalanobis distance is a statistical measurement of the distance between a point and a distribution. It is often used in outlier detection, where it can identify abnormal data points that do not fit the expected pattern. By calculating the Mahalanobis distance for each data point and comparing it to a threshold, it is possible to identify outliers and remove them from the dataset. This process can be particularly useful in machine learning and data analysis, where the accuracy of the results depends on the quality of the data. Therefore, by updating the data within the calculation of the Mahalanobis distance, we can effectively remove any outliers and enhance the accuracy of our analysis.