In machine learning, encoding refers to the process of converting categorical data into a numerical representation that can be easily processed by machine learning models.
Enable processing of categorical data: Machine learning models typically work with numerical data, so categorical data must be encoded into a numerical representation before it can be processed by the model.
Improve model performance: Encoding categorical data can improve the performance of machine learning models. For example, one-hot encoding can help prevent the model from assuming a false order or relationship between categories, while ordinal encoding can help preserve the relationship between categories. Choosing the appropriate encoding method can make a significant difference in the accuracy of a machine learning model.
Overall, encoding categorical data is an important step in machine learning that helps enable processing of categorical data, preserves information, avoids bias, and improves model performance.
- Without Use Any Encoding Techniques
- Label Encoding
- One-Hot Encoding
- Ordinal Encoding