The independence weight coefficient method can be used for feature selection, which selects the most representative features from a given dataset. In practical applications, the independence weight coefficient method can be used to calculate the weight of each feature, and then select the feature with higher weight as the input variable of the model. This can reduce the number of features and improve the accuracy and efficiency of the model. The independence weight coefficient method can also be used To explore the relationships between various variables in the dataset, in order to help us better understand the data.

    Data description:

    The following are various commonly used methods for generating variables:

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