This is the complete guide to understanding dimensions in machine learning. (New to ML ? ) Dimension in Machine Learning The number of input variables (or feature columns) in the given dataset i s termed as dimensions in machine learning. Example: Salary of employees based on designation and year of experience. Emp_num Designation Years_of_experience Salary in 1000$ 51 Software Engineer 2 15 108 Software Developer 5 45 67 Software Tester 4 28 89 Data Analyst 5 50 Here, there are 3 feature or input variables. And hence dimension is 3 in this case. In the above example Enum, designation, and year_of exp are the feature columns and Salary in 1000$ is the label or output column. What happens if you have high dimensions in the given dataset? This would be the same usual problem for the machine learning model as well to identify the patterns or relationships. Example: Salary of employees based on designation Emp_num Designation Emp_Age Gender Years_