How Does Treating Categorical Variables as Continuous Benefits?

Introduction

In machine learning, the performance and accuracy of the model completely depend n the data that we are feeding to it, and hence it is the most influential parameter in model training and model building. Mainly while dealing with the supervised machine learning problems, we have mostly categorical and continuous variables in the dataset. There are some benefits of converting categorical variables into continuous variables.

In this article, we will discuss some of the benefits of converting categorical variables to continuous variables, how it affects the model's performance, and what is the core idea behind doing so. This article will help one to understand the benefits of this transformation and will help answer interview questions related to the same.

Now Let us discuss the benefits of transforming variables to top continuous values.

Improved Performace

Machine learning algorithms require continuous variables as the training and testing data to be trained on and to predict on. We can not feed the categorical values to the algorithms in order to train and test the model.

Here in such cases, we can use encoding methods, such as one hot encoding, label encoding, and ordinal encoding, where the categorical variables can be transformed into continuous variables.

For example, we can take a dataset of the shopping behavior of the customers. Here in such a dataset, we will have columns like customer age, gender, occupation, salary, etc. Now the gender and the occupations will be a categorical column in this case, and hence it needs to convert into continuous variables.

We can use one hot fencing on the variable that does not have any order in the classes or label of the column, such as gender, where the classes or the label will not be of any order. Whereas ordinal encoding can be used when there is an order present in the classes of the variables.

Here by encoding these categorical columns to the numerical or continuous variables, we can easily feed these values to the algorithms such as linear regression and neural networks and can get reliable and high-perming models.

Feature Engineering

Feature engineering is the process in machine learning which is one of the most important steps before training the model on the dataset. Here the data is observed very well, it is visualized, analyzed, and according to the insights from the same, the features of the dataset are refined, removed, or added.

The transformation of categorical variables into continuous variables also helps in feature engineering, where we can extract new features from the dataset with the help of the same.

For example, suppose we have a geological dataset where there is a piece of information about different countries. In that case, we can transform these countries' information into numerical variables and then calculate the similarity between two different countries and can analyze better with the numerical form of the countries' features.

Issue of Sparsity

In some of the datasets, we have both categorical and continuous variables. Now here, it may happen that we have categorical variables that have many classes or many labels, but each class has a very low number of observations contained to it.

Now the machine learning model requires a significant amount of data and information in order to be accurate. Here each class of the categorical values has lower data observations, the model would not be able to find any statistical relationship between the categorical column and the target column, and hence the model's performance will be poor as there will be an issue of sparsity.

In such cases, we can use the encoding or the target encoding, where categorical variables are converted into continuous variables, and each category is treated as the average target value.

Capturing Non-Linear Relationships

In machine learning, the features that affect the target variables most are treated as the best features and are given the highest weightage. Now the relationship between every feature and the target variable may not be linear and hence we are required to identify the shape or the degree of relationship between the target variable and the features so that we can have an idea about the importance of the different features.

Now if we have a categorical variable as the feature in the dataset, we can not get an idea about the nonlinear relationship of the features and target variables, but if we transform this variable to continuous, we can use the polynomial or spline relationship where it helps in identifying the nonlinear relationships.

For example, suppose we have a dataset that has age groups and purchase behaviors of the user. In that case, we can convert the age group to a continuous variable, and we can easily use the polynomial degree relationship to identify the relations and best features for the training of the model.

Ordinal Information

In some of the categorical variables, we have ordinal information, where the classes of the categorical variables are in some order. We can encode these types of variables into continuous variables where the highest order of the variable can treat as the highest weightage in the scope of the variable only.

For example, if we have a categorical variable such as education level, and if it has classes like B.Tech, M.Tech, and Ph.D., then we can encode this type of variable into continuous variables, and we can also keep the order of the classes where the Ph.D. class will have higher weightage in the numerical values, and hence the model can understand that Ph.D. class is higher in order with either class and hence we can keep the essence of the data as it is.

Key Takeaways

• By transforming the categorical values into continuous values, we can enhance the accuracy and performance of the model.

• The transforming of categorical values into continuous variables helps keep the original information of the data.

• The encoding of categorical values also helps a lot in feature engineering and feature extraction.

• The categorical to a continuous transformation of the variables helps reduce the sparsity in some of the datasets.

• We can also capture the nonlinear relationships between the features and target variables of the dataset with the help of the transformation of categorical variables into continuous ones.

Conclusion

In this article, we discussed the benefits of transforming categorical variables into continuous variables, why it is important, and what is the core idea behind the same. This article will help one to understand the importance of such transformation and will help one to answer interview questions related to the same very easily and effectively.