Then in these new columns, the presence of that value in the row is denoted by 1, and the absence is denoted by 0. In this technique, first of all, for each distinct value of the feature, new columns are created. if we have continents of the world such as, after using Label Encoder these categorical values will be encoded as. In label encoding, each distinct value of the feature is assigned numeric values starting from 0 to N-1 where N is the total number of distinct values.Į.g. It should be noted however that tree-based machine learning algorithms like Decision Trees and Random Forest can work on categorical data and you may not be required to do categorical data encoding. So it becomes necessary to convert the categorical data into some sort of numerical encoding as part of data preprocessing and then feed it to the ML algorithms. cannot work with text-based categorical data. Most machine learning algorithms like Regression, Support Vector Machines, Neural Networks, KNN, etc.

Also Read – Types of Data in Statistics – A basic understanding for Machine Learning Why Categorical Data Encoding Needed in ML.

Colors can be denoted by numbers like Red = 1, Orange = 2, Blue = 3, and so on but it does not have any mathematical significance.

It is, however, possible that categorical data is denoted by numbers e.g. Usually, categorical data are non-numeric in nature and are text.

City: Mumbai, Delhi, Bangalore or Chennai, and so on.

Education qualification: High school, Undergraduate, Master’s or PhD.The common examples and values of categorical data are – Categorical data is the kind of data that describes the characteristics of an entity.

What is encoding in education