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C# Machine Learning Projects by Yoon Hyup Hwang

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Building a principal component classifier

In order to build a principal component classifier, which will flag those events that deviate from the normal connections, we need to calculate the distance between a record and the distributions of normal connections. We are going to use a distance metric, the Mahalanobis distance, which measures the distance between a point and a distribution. For the standardized principal components, like those here, the equation to compute the Mahalanobis distance is as follows:

Ci in this equation represents the value of each principal component, and vari represents the variance of each principal component. Let's ...

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