Level Encoding vs OneHot Encoding

Data preprocessing is a crucial step in machine learning. To feed data to a machine learning model, it must be cleaned, transformed, and normalized. Level Encoding and One-Hot Encoding are two frequently employed techniques in data preprocessing. Numerical data can be obtained from categorical data using either of these methods. However, they take different approaches, each with benefits and drawbacks.

In this article, we will explore both Level Encoding and One-Hot Encoding and discuss their pros and cons.

Level Encoding:

Level Encoding, sometimes referred to as Label Encoding, is a method for turning categorical data into numerical data by giving each category a distinct numerical value. When categorical data, like grades, ratings, or levels, has an intrinsic order or hierarchy, this method is especially helpful.

Let’s take an example where we have a dataset and a column called “size” that has the categories “red,” “blue,” and “yellow.” Level Encoding allows us to give these categories numerical values like 1, 2, and 3.

Level Encoding is an easy and effective process. It is simple to use and lowers the dataset’s dimensionality. Level Encoding does, however, have a serious drawback. It assumes the categorical data’s order, which isn’t necessarily true. Randomly allocating numerical values to categories could lead to poor model performance and mislead the model.

One-Hot Encoding:

A method for transforming categorical data into binary data is called One-Hot Encoding. Using this method, each category is represented by a binary vector, with a value of 1 for that category and 0 for all other categories.

Let’s take an example where we have a dataset and a column called “color” that has the categories “red,” “blue,” and “yellow.” ‘Red,’ ‘Blue,’ and ‘yellow,’ with binary values 1 or 0 denoting the presence or absence of each color, can be created as three additional columns using One-Hot Encoding.

One-Hot Encoding is useful when the categorical data has no inherent order. It does not assume anything about the data; instead, it maintains the information. However, One-Hot Encoding can result in a high dimensionality problem, which can lead to the curse of dimensionality. When a dataset has too many characteristics, it might be difficult to train the model leading to overfitting, which is known as the “curse of dimensionality.”

In conclusion, Level Encoding and One-Hot Encoding are popular preprocess techniques used to convert categorical data into numerical data. When there is an inherent order in the categorical data, Level Encoding is helpful; when there isn’t, One-Hot Encoding is helpful. Each approach has benefits and drawbacks, and the best approach will rely on the particular issue at hand as well as the available dataset.

In real-life applications, Level Encoding and One-Hot Encoding are frequently combined. For categorical data that has an intrinsic order, we can use Level Encoding; for categorical data that does not, we can use one-hot encoding. By balancing the benefits and drawbacks of both approaches, this hybrid strategy can enhance model performance.