Data preprocessing helps machine learning algorithms understand and analyze data for training models. Label Encoding is crucial for categorical data preparation. Label encoding turns categorical variables into numerical input for machine learning methods that only accept numbers. Label encoding’s goal, process, pros, cons, use cases, and alternatives will be covered in this article.
What is Label Encoding?
In machine learning, label encoding is a technique that transforms category input into numerical representation. Every separate category is given its own number. As an example, label encoding may assign 0 to “Red,” 1 to “Blue,” and 2 to “Green” in a “Color” feature that has the values “Red,” “Blue,” and “Green.” The data may be processed by machine learning algorithms thanks to this change. When categories in an ordinal data set have a natural order, label encoding performs best. However, because it could introduce erroneous associations between categories, it could not be appropriate for nominal data (data that lacks inherent order).
When to Use Label Encoding
Label encoding is best for numerical input algorithms like decision trees, SVMs, and most neural networks. Numeric data is needed to compute distance, gradient, and probability in many machine learning methods. To work with these algorithms, categorical data must be transformed to integers.
Label encoding works well for categorical data with a rank. The intensity of “low,” “medium,” and “high” conditions has an implicit order. Numbering these categories helps the algorithm grasp this underlying order. But it’s not suited for nominal categories without order, like automobile color or fruit type, because it could introduce spurious links between categories.
How does Label Encoder Work?
Label encoding is straightforward and involves these steps:
Identifying Categorical Columns
First, identify your dataset’s categorical variables or columns. These columns usually contain non-numerical data like “Country,” “City,” or “Product Type.”
Assigning Numeric Labels
For each classified column category, assign an integer. Categories are numbered starting with 0 and grouped alphabetically in lexicographical order. In the event that the column contains the words “Cat,” “Dog,” and “Elephant,” label encoding allocates 0, 1, and 2.
Replacing the Categorical Data
Once categories have been given numerical labels, substitute numbers for the categorical data. This means that “elephant” will be changed to two, “dog” to one, and “cat” to zero.
Model Training
Transforming the dataset prepares it for machine learning models. Numeric labels can be used by machine learning algorithms for training.
Advantages of Label Encoding
For categorical data, label encoding has many advantages:
- Simplicity and Efficiency: Label encoding is simple and requires no fancy algorithms or changes. It is efficient for many machine learning workflows since it is easy to implement and uses few computational resources.
- Preservation of Data Information: Label encoding creates one column for each category, unlike one-hot encoding. Avoiding the “curse of dimensionality” and shrinking the dataset can be advantageous when there are many categories.
- Works Well with Ordinal Data: Label-encoding works well for ordinal data with meaningful categories. Label encoding can retain the ranking of customer satisfaction levels (“Very dissatisfied,” “Dissatisfied,” “Neutral,” “Satisfied,” “Very satisfied”) and assist machine learning models understand this ordinal relationship.
Disadvantages of Label Encoding
Label encoding has limitations, especially when dealing with categorical data:
- Problem with Nominal Data: Label encoding can establish unwanted relationships for nominal data (such color or fruit type). If you label “Red” as 0, “Blue” as 1, and “Green” as 2, some machine learning algorithms may read this as Blue being closer to Green than Red. Since these colors are not ordered, this interpretation is incorrect.
- Potential for Misleading Interpretation: Label encoding assigns numerical values to categories, which linear regression and neural networks may read as a mathematical connection. An algorithm may treat 2 (for “Green”) as twice as important as 1 (for “Blue”), which could lead to inaccurate model predictions. So label encoding should be utilized carefully, especially with nominal data.
- Handling High Cardinality: Label encoding can work for categorical features with numerous unique categories (high cardinality), but it may cause confusion. For instance, a dataset with 1000 unique categories requires 1000 unique numeric labels, which doesn’t contribute much to the feature set in terms of dimensionality but could damage the model’s ability to generalize if the labels are mishandled.
Applications of Label Encoding
Label encoding is utilized in many machine learning tasks, especially to convert categorical data to numbers. Common applications include:
- Text Classification: Labeling words or phrases is common in text classification. Numbers can be used to classify documents by topic. In sentiment analysis, “positive,” “neutral,” and “negative” can be expressed as integers, making this valuable.
- Image Classification: Labels like “cat,” “dog,” and “bird” can be represented as numbers to train numerical picture classification algorithms. Machine learning models may assign “cat” 0, “dog” 1, and “bird” 2.
- Predictive Modeling: Labeling can convert categorical input features into numeric data for algorithms like decision trees, logistic regression, and others to predict outcomes, such as loan approval based on income bracket or customer churn based on subscription level.
Alternatives to Label Encoding
Label encoding is popular and straightforward, although other approaches for encoding categorical data may be better. This includes:
One Hot Encode Labels
Instead of label encoding, one-hot encoding creates a binary column for each data type. It eliminates ordinal correlations for nominal data but increases dataset dimensionality. This method works well for nominal data without order.
Binary Label Encoding
Label and one-hot encoding are combined in binary encoding. After converting categories to integers, it turns them to binary. This method reduces dimensionality compared to one-hot encoding, making it useful for many categories.
Target Encoding
The mean of the target variable for each category is used to encode categories. This method reduces dimensionality and is useful when the category and target are strongly correlated.
Conclusion
Label encoding is a key machine learning approach for numericalizing categorical input. This straightforward, quick technique works well for ordinal data or meaningfully ordered groups. Label encoding can establish unwanted category associations in nominal data, affecting model performance. In such circumstances, one-hot or binary encoding may be better.
Data type and machine learning model determine encoding method. Label encoding’s pros and drawbacks must be considered in the context of the challenge to ensure that the transformation helps the model’s learning.