When we talk machine learning, encoding is often mentioned as a very important preprocessing step. But how necessary is it, really? Is it just an extra burden, or does it truly impact model performance? In this blog post, we’ll look into encoding, its influence on machine learning models, different encoding techniques, and when to use them. By the end, you’ll see whether encoding is indispensable — or if its importance is just hyped.
Machine learning models rely on numbers, but real-world data often includes categories, text, and other non-numeric formats. Encoding transforms categorical data into numerical form so that models can process it efficiently. However, improper encoding can introduce biases, misinterpret relationships, and even harm our predictions.
Before we start just imagine using Label Encoding on city names (e.g., Lagos = 0, Abuja = 1, Port Harcourt = 2). This mistakenly implies that Abuja is “greater” than Lagos, misleading the model unless of course relative to the dataset or scenario we do have such ordinality. Instead, One-Hot Encoding will be better suited for such categorical variables.
Encoding isn’t just a formality — it shapes how the model interprets data. Key considerations include:
- Interpretability: Numerical input is essential for most models.
- Feature Relationships: Proper encoding preserves relationships (e.g., temperature levels should reflect order).
- Dimensionality: Encoding can expand features, increasing computational cost.
- Model Accuracy: The right encoding prevents misleading patterns and improves predictions.
Various encoding techniques exist, each with specific use cases.
What it does: Assigns a unique integer to each category.
When to use: For mapping unique integers to categorical variables (e.g., “Low” = 0, “Medium” = 1, “High” = 2). The issue with label encoding is that since they are numbers the model may assign some sort of ordinality to them. So, we have to be careful the types of data we apply it to.
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
dataset['Size'] = le.fit_transform(dataset['Size'])
What it does: Creates binary columns for each category.
When to use: For nominal data with no ranking (e.g., “Color” = Red, Green, Blue). Here no natural order
import pandas as pd
dataset = pd.get_dummies(dataset, columns=['Color'])
What it does: Assigns ordered integer values to categories.
When to use: For ordered categories (e.g., satisfaction levels). This is actually similar to label encoding but it is specifically designed for ordinal data, in this we manually assign integers to categories based on their order. In this case we need to explicitly define the order of categories.
from sklearn.preprocessing import OrdinalEncoder
oe = OrdinalEncoder(categories=[['Low', 'Medium', 'High']])
dataset['Priority'] = oe.fit_transform(dataset[['Priority']])
Note:
- Use Label Encoding for nominal (unordered) categories only if the model can handle categorical data properly.
- Use Ordinal Encoding for ordinal (ordered) categories to retain meaningful ranking.
What it does: Replaces categories with the mean of the target variable. This is really useful when we want to capture the relationship between category and the target column.
When to use: For high-cardinality categorical variables and we wish to reduce dimensionality while still keeping information about the target column.
import category_encoders as ce
te = ce.TargetEncoder(cols=['City'])
dataset['City_Encoded'] = te.fit_transform(dataset['City'], dataset['Target'])
Encoding thousands of “Product IDs” using One-Hot Encoding results in excessive features. Target Encoding is a more efficient alternative.
What it does: Replaces categories with their frequency in the dataset. (i.e., how often it appears in the dataset.
When to use: When category frequency carries useful information and you wish to reduce the number of unique categories present.
freq = data['Category'].value_counts(normalize=True)
dataset['Category_Freq'] = dataset['Category'].map(freq)
What it does: For this the categories are converted to numeric values using label encoding. Then these integers will be convertef into binary and each binary digit becomes a seperate column. Basically its the combination of label encoding and one-hot encoding
When to use: Use binary encoding when you have a large number of categories and want to reduce dimensionality compared to one-hot encoding.
import category_encoders as ce
be = ce.BinaryEncoder(cols=['Category'])
dataset = be.fit_transform(dataset)
- Ordinal Data: Use Label or Ordinal Encoding.
- Nominal Data: Use One-Hot or Binary Encoding.
- High Cardinality Data: Use Target or Frequency Encoding.
- Text Data: Use techniques like Bag of Words or Word Embeddings.
- Avoid Data Leakage: When using target encoding, ensure to calculate the target means on the training set and apply them to the test set to avoid data leakage.
- Handle Missing Values: Some encoding methods require imputation first.
- Scale Features: Post-encoding scaling can improve model performance.
- Experiment: Try different encodings and compare results.
Encoding is a fundamental step in the machine learning pipeline that can make or break your model’s performance. By understanding the different types of encoding and when to use them, data is represented in a way that allows your model to learn effectively. Remember, the key to successful encoding is to preserve the meaning and relationships within your data while making it understandable for the machine learning algorithms.
So, the next time you’re preparing your data for a machine learning project, ensure that you are making use of the best encoding method that will favor model performance and prediction.