labelenkoodauksella
Labelenkoodauksella, often translated as label encoding, is a data preprocessing technique used in machine learning. It involves converting categorical data into numerical representations that machine learning algorithms can understand. Many algorithms, such as linear regression, logistic regression, and support vector machines, require numerical input. Categorical data, which consists of distinct groups or labels (e.g., "red," "blue," "green"), needs to be transformed before it can be used in these models.
The process of label encoding assigns a unique integer to each category. For example, if a feature
While simple and efficient, label encoding can introduce an artificial ordinal relationship between categories where none
To mitigate this issue, one-hot encoding is often preferred for nominal (unordered) categorical data. However, for