Data drives each Machine Learning initiative; however, not all data contains numeric values. Consider features such as City, Gender, Product Type, and Payment Mode. All of these contain textual information, and computers are unable to understand language. This is why we require encoding.
Learning Data Science? Looking forward to joining any Certified Data Science Course in Pune? If yes, then Encoding is an important topic that cannot be ignored by anyone. We will discuss everything about encoding in this blog.
What Is Categorical Data?
It refers to the kind of data whose elements carry values such as labels and groups, rather than numeric data. The two broad categories of categorical data include nominal and ordinal data. In nominal data, the values have no order; some examples include color names, city names, and blood types. On the other hand, in ordinal data, there is an established order among the data points; examples include educational qualification levels.
Why Do We Need Encoding?
Machine Learning models deal with numeric variables only. When you input your data as text, many machine learning models would return errors or erroneous predictions because of the data type issue. Encoding refers to the conversion of categorical variables to numeric variables to allow machine learning models to train on the input.
Label Encoding
In label encoding, a numerical value is assigned uniquely to each individual label within any dataset or group. Delhi gets labeled as 0, Pune as 1, and Mumbai as 2.
However, here comes a caveat. The algorithm might rank Mumbai (2) as “greater” than Delhi (0), despite there being no inherent ranking between them. Hence, label encoding should be used for classification tasks or with tree-based algorithms such as Random Forest.
One-Hot Encoding
One-Hot Encoding creates an extra column for each distinct value of a category type. Each column represents 1 if the value exists and 0 otherwise. For example, if your city column has three cities, you will end up with three columns.
The procedure doesn’t generate artificial ordering; hence, it is suitable for nominal variables. It works perfectly fine with linear regression, logistic regression, and neural networks. However, its disadvantage lies in terms of memory usage since, in case you have a categorical variable with 500 categories, you end up generating 500 more columns!
Ordinal Encoding
Ordinal Encoding should be used in case the variables have an intrinsic order. You assign numerical values that reflect this order, i.e., low = 1, medium = 2, and high = 3. These numerical values have some significance; thus, your algorithm will know which value is greater than others. Make sure you define the order manually at all times since machine learning tools tend to sort labels alphabetically by default.
Frequency Encoding
With Frequency Encoding, we convert categories to their occurrence numbers within the dataset. If the city occurs 200 times, its value would be 200. This technique is easy, efficient, and highly useful for columns having numerous categories.
Target Encoding
Target encoding entails substituting each individual category with the average target value within that particular category. For instance, while dealing with a loans dataset, we could substitute each individual city with the mean default ratio associated with that particular city.
Target encoding works well when there are numerous categories. Nevertheless, it leads to data leakage when not done properly.
Binary Encoding
The Binary Encoding technique converts categories into numeric values and further into binary, splitting the digits across separate columns. The Binary Encoding technique uses far fewer columns compared to One-Hot Encoding. Thus, it acts as a balance point between One-Hot and other techniques.
How to Choose the Right Technique
Here is a simple guide:
- Use ordinal encoding for ordered data.
- Use one-hot encoding for a few categories with no order.
- Use frequency, target, or binary encoding when there are many categories.
- Use label encoding for target labels or tree-based models.
Conclusion
Although encoding might seem like a simple process, it will define how much your machine learning model will learn. Selecting an appropriate method could help boost your performance, making you a more proficient data science specialist. Practicing your skills on projects supervised by professionals is the best strategy.
With the intention of acquiring skills for employment, you can join Data Science Training in Gurgaon, where you will be trained in programming languages such as Python, Machine Learning, and Data Preparation, among others. With this course, learners can make the best decisions using data analysis techniques. Join now and become a professional tomorrow!
