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1Encoding highcardinality string categorical variablesarXiv:1907.01860v4 cs. LG 5 Sep 2019Patricio CERFA and Gael Varoquaux AbstractStatistical models usually require vector representations of categorical
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How to fill out encoding high-cardinality string categorical

01
Identify the high-cardinality string categorical variable that needs to be encoded.
02
Determine the type of encoding that will be suitable for your dataset, such as one-hot encoding, label encoding, or target encoding.
03
For one-hot encoding, create dummy variables by creating a new binary column for each unique category in the variable.
04
For label encoding, assign a unique numerical value to each category in the variable.
05
For target encoding, replace each category with the mean target value of the corresponding category.
06
Handle missing values, if any, by either assigning a specific value or imputing them with appropriate techniques.
07
Scale or normalize the encoded variable if necessary.
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Verify the encoding results and assess their impact on the target variable through statistical analysis or machine learning models.

Who needs encoding high-cardinality string categorical?

01
Researchers, data scientists, and statisticians who are working with datasets containing high-cardinality string categorical variables.
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Encoding high-cardinality string categorical is the process of converting categorical variables with a large number of unique values into a format that can be used for analysis or machine learning models.
Data analysts, data scientists, or anyone working with data sets containing high-cardinality categorical variables may be required to use encoding techniques such as one-hot encoding or target encoding.
To fill out encoding high-cardinality string categorical, one must first identify the categorical variables with a large number of unique values, then choose an appropriate encoding method such as one-hot or target encoding.
The purpose of encoding high-cardinality string categorical is to transform categorical variables with many unique values into a numerical format that can be easily processed by machine learning algorithms.
The information that must be reported on encoding high-cardinality string categorical includes the categorical variables being encoded, the encoding technique used, and any transformations applied to the data.
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