[MXML-8-04] Random Forest [4/7] - Proximity Matrix, Missing Value imputation

* This video was produced in Korean and translated into English. And the voice is generated by AI TTS. The English translation may contain grammatical errors.
This is part 4 of a series on Random Forest.
In this video, we will look at how to handle missing values in dataset. Let's take a look at the proximity matrix and how to use it to handle missing values in the training and test data.
If your data has missing values, you can use Random Forest to estimate the missing values. A proximity matrix is used for that purpose.
The basic idea is to estimate the missing values ​​of a data point by referencing data points that are similar to that data point.The proximity matrix is used to measure the similarity between data points. And since similarity is the inverse of distance, one minus the proximity matrix is the distance matrix.
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