How can discrete-varying covariates be effectively dealt with in a model predicting an outcome?

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There are several ways to effectively deal with discrete-varying covariates in a model predicting an outcome:

One-Hot Encoding: One-Hot Encoding is a technique to convert categorical variables to numerical variables. It creates a binary variable for each unique category of the discrete variable. For example, if we have a discrete variable for color with categories as ‘Red’, ‘Yellow’ and ‘Green’, it will create three variables as ‘RedOrNot’, ‘YellowOrNot’, ‘GreenOrNot’. These variables take a value of 1 if the record has that category, and 0 otherwise. This technique helps to capture the effects of discrete variables in the model.

Dummy Coding: Dummy coding is another technique to convert categorical variables to numerical variables. It creates a variable for each unique category of the discrete variable, with one category as reference. For example, if we have a discrete variable for color with categories as ‘Red’, ‘Yellow’ and ‘Green’, we will choose ‘Red’ as the reference category, and create two variables as ‘Yellow’ and ‘Green’. These variables take a value of 1 if the record has that category, and 0 otherwise. This technique helps to capture the overall effect of the discrete variable in the model.

Effect Coding: Effect coding is similar to dummy coding but instead of taking one category as reference, it takes the mean of all the categories as reference. This can be useful if the categories have a relative order, and we want to know the effect of each category relative to the mean of all categories.

Leave-One-Out Encoding: Leave-One-Out encoding is a technique to encode categorical variables by substituting the average of the output variable for each category with the output for that category removed. This method eliminates any spurious correlation between categories and the output variable when used as a predictor in regression.

Target Encoding: Target Encoding is a technique to encode categorical variables by substituting the mean of the output variable for each category. This technique can also be effective in capturing the effects of discrete variables in the model, especially if the categories have a strong relationship with the output variable.

Overall, the choice of encoding technique will depend on the data and the specific research question. It’s often a good idea to try different encoding techniques and compare the performance of each in the model.

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Asked: ** 2022-03-14 11:00:00 +0000 **

Seen: **1 times**

Last updated: **Nov 10 '22**

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