One Hot Encoding vs Label Encoding (original) (raw)

Last Updated : 22 Jan, 2026

Machine learning models require numerical input to make predictions but real-world datasets often contain categorical data such as countries, colours or severity levels. Encoding techniques convert these categorical variables into numerical formats that models can interpret effectively.

Label Encoding vs One-Hot Encoding

Understanding One-Hot Encoding

One-Hot Encoding converts each category of a categorical variable into a new binary column. Each column represents a unique category where a value of 1 indicates the presence of that category and 0 indicates its absence.

Features of One-Hot Encoding

• Best suited for nominal data where categories have no inherent order (e.g., colors, countries).
• Creates multiple binary features increasing the dimensionality of the dataset.
• Easy to interpret as each column directly represents a category.
• Works well with algorithms that do not assume ordinality such as logistic regression, neural networks and KNN.
• May lead to sparse data and higher memory usage if there are many unique categories.

When to use

• The categorical variable is nominal.
• The number of unique categories is relatively small.
• The algorithm does not assume an ordinal relationship.
• Avoid using it with high-cardinality features to prevent the curse of dimensionality.

Implementation of One-Hot Encoding

Here we do One-Hot Encoding using Pandas. It converts the categorical Country column into separate binary columns, where 1 indicates the presence of a country and 0 indicates its absence

Python `

import pandas as pd

countries = ['USA', 'Canada', 'India', 'USA', 'Canada'] df = pd.DataFrame({'Country': countries})

one_hot = pd.get_dummies(df['Country'], dtype=int) print(one_hot)

`

**Output:

One-Hot Encoding

Understanding Label Encoding

Label Encoding assigns each category of a categorical variable a unique integer value. This converts the categorical column into a single numerical feature.

Features of Label Encoding

• Best suited for ordinal data, where categories have a natural order (e.g. "Low", "Medium", "High").
• Creates a single column keeping the feature space compact.
• Easier for tree-based models like Decision Trees and Random Forests which can handle ordinal relationships effectively.
• Can be misinterpreted by models that assume numeric relationships between categories where none exists.
• More memory-efficient than One-Hot Encoding.

When to use Label Encoding

• The categorical variable is ordinal.
• Preserving the order of categories is important.
• Using tree-based algorithms like Decision Trees, Random Forests or XGBoost.
• Memory efficiency is a priority.

Implementation of Label Encoding

Here we implement Label Encoding using scikit-learn. It converts the categorical Severity column into numeric values, assigning a unique integer to each category while preserving the ordinal relationship.

Python `

import pandas as pd from sklearn.preprocessing import LabelEncoder

severity = ['Low', 'Medium', 'High', 'Medium', 'Low'] df = pd.DataFrame({'Severity': severity})

label_encoder = LabelEncoder() df['Severity_encoded'] = label_encoder.fit_transform(df['Severity']) print(df)

`

**Output:

Label Encoding

One-Hot vs Label Encoding

Here we compare One-Hot Encoding with Label Encoding: