Comparing One Hot Encoding vs Categorical Encoding vs Label Encoding Using Python
One-Hot Encoding, Categorical Encoding, and Label Encoding are methods used to convert categorical data into numeric form for machine learning models. One-Hot Encoding creates binary columns for each category, making it suitable for nominal categorical features without inherent order, though it can significantly increase dimensionality with high cardinality categories. Categorical Encoding, often used for high-cardinality variables, can preserve relationships between categories and may be more memory-efficient, as it reduces dimensionality compared to One-Hot Encoding. Label Encoding assigns a unique integer to each category and is ideal for ordinal data, where the order matters, or binary classification problems. In Python, One-Hot Encoding can be applied using pandas' get_dummies, Categorical Encoding with libraries like category_encoders, and Label Encoding using LabelEncoder from scikit-learn. These encoding techniques are tested on real-world datasets such as loan data, where features like "emp_title," "state," and "loan_status" are encoded for machine learning models. Each encoding method has its pros and cons, with One-Hot Encoding increasing the number of features, while Label Encoding and Categorical Encoding manage the dimensionality differently. For ordinal data like loan grades, Label Encoding is most effective, whereas One-Hot Encoding is better for categorical data like "state" or "loan purpose." In practice, the choice between these methods depends on the data's nature, the model’s requirements, and the need for preserving relationships between categories. Finally, the performance of models trained with each encoding method, such as accuracy and F1 score, is evaluated using machine learning models like Random Forest.
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