Скачать или смотреть Encoding Categorical Variables

@genaiexp Encoding categorical variables is a crucial step in data preprocessing, particularly for algorithms that require numerical input. Techniques like one-hot encoding and label encoding transform categorical data into a numerical format, allowing models to interpret them effectively. One-hot encoding creates binary columns for each category, ideal for nominal variables. Label encoding assigns unique integers to each category, suitable for ordinal data. Handling high cardinality is essential, as excessive categories can lead to overfitting. Choosing the right encoding method depends on the dataset's nature and the model's requirements, ensuring that the data is accurately represented and enhances model interpretability.