Data Preprocessing Questions Medium
In data preprocessing, categorical variables are handled differently compared to numerical variables. Categorical variables represent qualitative data and can take on a limited number of distinct values or categories. Here are some common approaches to handle categorical variables:
1. Label Encoding: In this method, each category is assigned a unique numerical label. For example, if we have a categorical variable "color" with categories "red," "blue," and "green," we can assign them labels 0, 1, and 2, respectively. However, this method may introduce an arbitrary order or hierarchy among the categories, which may not be desired in some cases.
2. One-Hot Encoding: This technique creates binary columns for each category, where a value of 1 indicates the presence of that category and 0 indicates its absence. For example, using the "color" variable, we would create three binary columns: "red," "blue," and "green." If an observation has the category "red," the "red" column would have a value of 1, while the other columns would be 0. One-hot encoding avoids introducing any arbitrary order among the categories.
3. Ordinal Encoding: This method is suitable when there is an inherent order or hierarchy among the categories. The categories are assigned numerical values based on their order. For instance, if we have a variable "education" with categories "high school," "college," and "graduate," we can assign them values 0, 1, and 2, respectively. However, caution should be exercised to ensure that the assigned values truly reflect the order and do not introduce any bias.
4. Binary Encoding: This technique converts each category into binary code, which is then split into multiple binary columns. Each column represents a bit of the binary code. For example, if we have three categories, we would create three binary columns, and each category would be represented by a unique combination of 0s and 1s.
5. Frequency Encoding: In this approach, each category is replaced with its frequency or occurrence count in the dataset. This method can be useful when the frequency of a category is informative for the analysis.
It is important to note that the choice of encoding method depends on the nature of the categorical variable, the specific problem, and the machine learning algorithm being used. Additionally, categorical variables may also require other preprocessing steps such as handling missing values, dealing with rare categories, or feature scaling, depending on the specific requirements of the analysis.