Machine learning algorithms understand only numbers. So, if a column in a dataset contains categorical values, we need to encode the categorical values into numbers. We can use label encoding for that purpose. In label encoding, labels are replaced by integers. So, it...
In the missing category imputation, we fill in the missing values in a categorical column with the word “Missing.” For example, let’s read the titanic dataset. As we can see from the following program, the dataset has some missing values in the embark town column....
In our previous articles, we discussed mean or median imputation, end-of-distribution imputation, and frequent category imputation. But, sometimes we may want to fill in missing numerical values with numbers that indicate missing values. For example, if we are...
What is the frequent category imputation in machine learning? If a column contains only numerical data, then we can use mean or median imputation to fill in the missing values of the column. But if a column contains categorical values, then we mostly use most frequent...
What is the end-of-distribution imputation? If data in a numerical column are missing randomly, then mean or median imputation is a good technique. But, if data are not missing randomly, then we may want to perform end-of-distribution or end-of-tail imputation. In the...
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