ML: Categorical Encoding Showdown

Categorical data poses a unique challenge in machine learning. Algorithms often require numerical inputs, but categorical variables contain non-numeric information. To bridge this gap, encoding techniques like one-hot encoding and label encoding come into play. Let’s explore these methods with Python examples to understand their differences and use cases.

One-Hot Encoding

One-hot encoding is a method that converts categorical variables into a binary matrix. Each category becomes a binary column, where only one bit is ‘hot’ (1) while the others remain ‘cold’ (0).

In this example, the categorical variable ‘Color’ is transformed into binary columns. Each row represents an instance, and the ‘1’ in the respective column indicates the color present.

Label Encoding

Label encoding assigns a unique numerical label to each category, effectively converting categorical data into numerical format.

Here, ‘Red’ is represented as 2, ‘Green’ as 1, and ‘Blue’ as 0. Each category gets a distinct numerical label.

One-Hot Encoding vs. Label Encoding

Dimensionality: One-hot encoding increases the number of columns, potentially causing high dimensionality. Label encoding keeps the data in a single column, reducing dimensionality.

Ordinality: One-hot encoding does not assume any ordinal relationship among categories, while label encoding introduces ordinality due to the assigned numerical labels.

Use Cases

Use one-hot encoding when dealing with unordered categorical data, like color or country names, where there’s no inherent order.

Label encoding might be preferable for ordinal categorical data, such as low, medium, and high, where there’s a clear order.