Choosing Between One-Hot and Label Encoding

BetterLife
2 min readNov 11, 2023
Photo by Pietro Jeng on Unsplash

Categorical variables are a common component of datasets, and encoding them is a crucial step in preparing data for machine learning models. Two popular techniques for encoding categorical variables are one-hot encoding and label encoding. Let's delve into both methods, providing examples and discussing when to use each.

One-Hot Encoding:

Definition: One-hot encoding is employed when categorical variables lack an inherent order or ranking, and each category is treated as independent.

Example: Consider a dataset with a 'Country' column containing three categories: 'Germany,' 'India,' and 'France.' Applying one-hot encoding results in three binary columns:

| Germany | India | France |
|---------|-------|--------|
| 1 | 0 | 0 |
| 0 | 1 | 0 |
| 0 | 0 | 1 |

Pros:

  1. Preservation of Independence: Maintains the independence of categories.
  2. Compatibility: Suitable for algorithms that can handle sparse matrices.

Cons:

  1. Dimensionality: This can lead to a high-dimensional dataset.
  2. Sparse Matrix: This may result in a sparse matrix with increased memory usage.

Label Encoding:

Definition: Label encoding is appropriate when there exists an ordinal relationship among categories, implying a meaningful order.

Example: For a 'Size' column with categories 'Small,' 'Medium,' and 'Large,' label encoding could look like:

| Size |
|------|
| 0 |
| 1 |
| 2 |

Pros:

  1. Compact Representation: Reduces dimensionality, providing a more compact representation.
  2. Suitable for Ordinal Variables: Useful when categories have a meaningful order.

Cons:

  1. Misleading Ordinality: May introduce unintended ordinality when applied to nominal variables.
  2. Algorithm Sensitivity: Some algorithms might interpret the numerical labels as having mathematical significance.

Choosing the Right Encoding:

Example: Suppose we have a dataset with a ‘Temperature’ column having categories ‘Low,’ ‘Medium,’ and ‘High.’ Here, the order is meaningful — ‘Low’ < ‘Medium’ < ‘High.’ In this case, label encoding might be suitable:

| Temperature |
|-------------|
| 0 |
| 1 |
| 2 |

Considerations:

  1. Nature of Data: Consider whether the categorical variable has an ordinal relationship. For ‘Country,’ use one-hot encoding; for ‘Temperature,’ use label encoding.
  2. Algorithm Compatibility: Some algorithms can handle categorical variables directly, reducing the need for encoding.

Best Practices:

  1. Understand Data: Carefully analyze the nature of the categorical variable to determine the appropriate encoding method.
  2. Evaluate Impact: Consider the impact of encoding on dimensionality and algorithm behavior.

In conclusion, the choice between one-hot encoding and label encoding depends on the nature of the data and the characteristics of the categorical variable. Understanding the distinctions between these methods is crucial for effective preprocessing and building accurate machine-learning models.

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