- Introduction-
In machine learning, the input data or the learning dataset is in a form of numerical data.
But there can be many cases where these input datasets are categorial instead of numerical.
Feature encoding is a popular method used to convert the categorial data into numerical format. Encoding categorical data allows the machine learning models/algorithms to interpret and use the data effectively and efficiently.
- Types of Encoding Techniques-
Following are the most commonly used Feature Encoding Techniques-
- One-Hot Encoding: This technique is used to convert categorical variables into a binary matrix (i.e. 0 or 1), where each category becomes a binary feature. Each category is represented by a column, and for each observation, only one of the columns will have a value of 1, indicating the presence of that category.
- Binary Encoding: Binary encoding involves first converting categories into integers and then converting those integers into binary code. Each binary digit becomes a feature, resulting in fewer columns compared to one-hot encoding.
- Hashing: Hashing techniques map categorical variables into a fixed-size space using hash functions.
- Target Encoding: Also known as mean encoding, target encoding replaces each category with the mean of the target variable for that category.
- Ordinal Encoding: Similar to label encoding, ordinal encoding assigns integer values to categories, but it preserves the order of the categories.
Python’s Pandas library provides rich set of functions to implement these feature encoding techniques efficiently.
Choosing the appropriate feature encoding technique depends on the nature of the data, the algorithm being used, and the specific requirements of the problem.