Suraj Saini
Suraj Saini
Categorical Feature Encoding in SAS (Bayesian Encoders)
What is Bayesian Encoding? Bayesian Encoding is a type of encoding that takes into account intra-category variation and the target mean when encoding categorical variables. It is a type of targeted encoding that comes with several advantages. For example, Bayesian Encoding requires minimal effort compared to other encoding methods. In this blog post, we talk […]
5 Categorical Feature Encoding Techniques in SAS
What is Categorical Feature Encoding? Categorical variables are usually represented as strings in limited numbers while categorical feature encoding is the process of converting data into a format understandable by machine learning models. The performance of machine models depends on several factors. One factor that determines performance of the models are the methods used to […]
Techniques for Feature Scaling and Transformations with Custom SAS Macros
In a previous blog post, we talked about feature scaling techniques and implemented this in Base SAS using SAS Macros. However, in this blog post, we are going to focus on feature scaling and transformation techniques using SAS Macros. Here I will explain what they are and the scenarios they are best applied to. 5. […]
Techniques of Feature Scaling with SAS Custom Macro
What is Feature Scaling? Feature scaling is a process that is used to normalize data, it is one of the most preponderant steps in data pre-processing. Feature scaling is done before feeding data into machine learning, deep learning and statistical algorithms/models. In most cases, it has been noticed that the performance of the models increases […]
How SAS Custom Macro make feature engineering easier
1. One Hot Encoding There are many different definitions for one-hot encoding on the internet. In general words, it is the process of converting categorical variables into binary form (1’s and 0’s), which can be fed into machine learning, deep learning, and statistical algorithms to make better predictions or improve the efficiency of the ML/DL/Statistical […]