What are some techniques for handling categorical columns in a dataset that have been ordinal encoded?

Ordinal encoding is a technique used to represent categorical data as numerical values. It assigns a unique integer value to each category based on its order or rank within the dataset. For instance, if we have a column representing educational degrees with categories like "High School", "Bachelor's", and "Master's", we can assign values 1, 2, and 3 to these categories respectively.

Once the categorical columns are ordinal encoded, we can use various techniques for handling them, depending on the problem we are trying to solve. Here are some common techniques:

• Scaling: Ordinal encoding assigns numerical values to categories based on their order, but the scale of these values may not be meaningful. For instance, if we assign values 1, 2, and 3 to the categories "High School", "Bachelor's", and "Master's" respectively, the difference between 1 and 2 may not be the same as the difference between 2 and 3. In such cases, we can scale the encoded values to make the scale meaningful. One common technique is to normalize the values using min-max scaling or z-score normalization.

• One-Hot Encoding: One-hot encoding is another technique for handling categorical data. It creates a binary vector for each category, where the vector has a value of 1 in the position corresponding to the category and 0 elsewhere. For example, the "education" column with values "High School", "Bachelor's", and "Master's" can be represented as three binary vectors: [1, 0, 0], [0, 1, 0], and [0, 0, 1] respectively.

• Feature Engineering: Feature engineering is the process of creating new features from existing ones. We can use ordinal encoded columns to create new features that capture additional information about the data. For instance, we can create a feature that represents the difference between two ordinal encoded columns or a feature that represents the ratio of two ordinal encoded columns.

• Model-based techniques: The choice of technique for handling ordinal encoded categorical variables may also depend on the machine learning model we are using. For instance, tree-based models like Random Forest and XGBoost can handle ordinal encoded variables directly without any preprocessing. In contrast, linear models like logistic regression may benefit from scaling or one-hot encoding.

In summary, ordinal encoding is a useful technique for representing categorical data as numerical values. We can use various techniques like scaling, one-hot encoding, feature engineering, and model-based techniques to handle ordinal encoded columns depending on the problem we are trying to solve.