Label Encoding is a technique used for preprocessing categorical data. It is commonly used in machine learning pipelines in order to encode non-numeric values so they can be used by algorithms that only work with numerical inputs. Label encoding converts a categorical input into numerical labels based on the alphabetical order of the labels.
Label encoding is a simple and intuitive data preprocessing strategy that can be applied to dataset columns with categorical data. The technique assigns a unique numerical identifier (or label) to each category of input. For instance, in a dataset with COLORS column with values ‘red’, ‘green’ and ‘blue’, the label encoder will assign the labels 0, 1 and 2 respectively.
Label encoding has two main advantages; firstly, it is more intuitive than other data preprocessing techniques because the numerical labels reflect the value of the categories used in the dataset. Secondly, label encoding preserves the magnitude of the inputs, i.e. two labels that occur closely together in the lexicographical order will be assigned numerical labels that differ by a small amount, which can be useful when modeling the data with algorithms.
Conversely, label encoding also has some major drawbacks. Firstly, by assigning numerical labels to categorical inputs, the mapping is not always intuitive and can cause bias in the algorithm that models the data. Secondly, labeled encoded data cannot be used for regression-based algorithms (such as linear or logistic regression); since the numerical labels do not convey any meaning, the algorithm cannot make predictions based on the magnitude of the labels.
Label encoding is a useful data preprocessing technique for preparing categorical inputs for further processing. However, care must be taken to ensure that the mapping of numerical labels to categorical values is not unintentionally biasing the model used to fit the data.