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What is the Exact Algorithm used below to derive the Drift magnitude as a percentage? And how did they get these percentages for Top Drifting Features?

This is a sample dashboard for Azure Drift Detection in Data: enter image description here

Azure has specified these algorithms below for each categorical and numerical feature: enter image description here

But none of them return a percentage. And mathematically the Wasserstein distance (Earth-Mover Distance) can be any number from 0 to infinity. So how do they derive a percentage out of it?

There was a mention of the Matthews correlation coefficient (MCC) used for Drift magnitude. If so how does that work exactly?

Imperial_J
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1 Answers1

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The data drift will be working on time series dataset. The time series dataset will work on distance methodology. The distance methodology is more like to be using with cluster. The clusters are working on distance pattern.

While creating the data drift, we are running it on an instance cluster. The machine learning model K-nearest neighbor. LSTM and dynamic time wrapping (DTW) will be working internally in the data drift.

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While uploading the dataset the properties must be shifted to Timestamp

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The data drift factors are measured using Min, Max, Mean, and other distance factors. Root mean squared methodology will be in front-line to take up the distance factors for evaluation operation.

Sairam Tadepalli
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