Description: Frequency encoding is a technique used in machine learning and statistics to transform categorical variables into numerical representations. This technique is based on the frequency of occurrence of each category within a dataset. Instead of assigning an arbitrary number to each category, frequency encoding assigns each category a value that represents how many times it appears in the dataset. This allows machine learning models to better interpret the information, as more frequent categories will have a higher value, which can be useful for identifying patterns and relationships in the data. Frequency encoding is particularly valuable in situations where categorical variables have numerous levels or categories, as it helps reduce the dimensionality of the dataset and mitigate overfitting issues. Additionally, this technique is easy to implement and can be combined with other data preprocessing methods, making it a versatile tool for improving the performance of predictive models.
