Label Encoding vs. One Hot Encoding

57) When to use a Label Encoding vs. One Hot Encoding?

This question generally depends on your dataset and the model which you wish to apply. But still, a few points to note before choosing the right encoding technique for your model:

We apply One-Hot Encoding when:

• The categorical feature is not ordinal (like the countries above)

• The number of categorical features is less so one-hot encoding can be effectively applied

• May increase overfitting

We apply Label Encoding when:

• The categorical feature is ordinal (like Jr. kg, Sr. kg, Primary school, high school)

• The number of categories is quite large as one-hot encoding can lead to high memory consumption

Label Encoding and One-Hot Encoding

In machine learning, especially in handling categorical data, it's crucial to convert text or categorical data into a numerical format that algorithms can understand. Label encoding and one-hot encoding are two common techniques for transforming categorical variables into numerical values.

Label Encoding

Label encoding converts each category value into a unique integer. This method is straightforward and efficient but introduces a new problem: the numerical values can be misinterpreted by the machine learning algorithms as having some sort of order or hierarchy when there is none. For example, if "red" is encoded as 1, "blue" as 2, and "green" as 3, the algorithm might assume that "green" is somehow more than "red," which is not a desirable trait for nominal (unordered) categorical data.

• Pros:
  • Simple to implement and understand.
  • Saves storage space.

• Cons:
  • The numerical assignment introduces an ordinal relationship that doesn't exist, potentially leading to poor performance for certain models, especially linear models.

• Use Cases: Suitable for ordinal categorical data or as a preprocessing step before applying one-hot encoding.

One-Hot Encoding

One-hot encoding converts categorical variables into a form that could be provided to ML algorithms to do a better job in prediction. It creates a binary column for each category and returns a matrix with the results. For example, if there are three categories (red, blue, green), one-hot encoding will create three features, each representing one of the categories. A category's presence is marked by 1 and absence by 0. Unlike label encoding, this method does not introduce an ordinal relationship.

• Pros:
  • Removes the ordinal relationship, allowing the model to treat each category with equal importance.
  • Often results in better performance for many models.

• Cons:
  • Can significantly increase the dataset's dimensionality if the categorical variable has many unique categories (known as the "curse of dimensionality").
  • Requires more storage space.

• Use Cases: Suitable for nominal categorical data where no ordinal relationship exists.

Implementation in Python

Label Encoding with sklearn

from sklearn.preprocessing import LabelEncoder

# Sample data
categories = ['red', 'blue', 'green', 'blue', 'red']

# Label encoding
label_encoder = LabelEncoder()
label_encoded = label_encoder.fit_transform(categories)

print(label_encoded)  # Output: array-like data with integers

One-Hot Encoding with pandas

import pandas as pd

# Sample data
df = pd.DataFrame({'color': ['red', 'blue', 'green', 'blue', 'red']})

# One-hot encoding
one_hot_encoded = pd.get_dummies(df, columns=['color'])

print(one_hot_encoded)

Conclusion

The choice between label encoding and one-hot encoding depends on the specific dataset and the type of model you are using. One-hot encoding is generally preferred for nominal categorical data in many machine learning models, especially linear models. However, for tree-based models, label encoding can be more efficient and performant. It's also worth considering the impact on dimensionality and computational resources when choosing the encoding method.