


All these add to the importance of encoding categorical values as the algorithm’s performance can vary based on how categorical variables are encoded which is nothing but encoding categorical data into numeric form before evaluation of the model. Data sets do contain categorical values which are usually in text format which machine learning models cannot assess and hence converting categorical data is an unavoidable activity. Though performance depends upon the model and its hyper-parameters, still data processing and feeding of variables are very important for result optimization. It takes hours of work to develop a machine learning algorithm.
