When is it compulsory to use the filter to change the data type to nominal? I am doing classification right now, and the results differ by a huge margin if I changed it to nominal compared to as it is. Thank you in advance.
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I don't your question is formed well but I will try to answer it anyway.
Nominal and numeric attributes represent different types of attributes and therefore are treated differently by machine learning algorithms.
Nominal attributes are limited to a closed set of values and they don't have order or any other relation between them. Usually nominal attributes should have a small amount of possible values (large set of possible values may cause over-fitting). The color of car is an example of an attribute that probably would be represented as a nominal attribute.
Numeric attributes are usually more common. They represent values on some axis and are not limited to specific values. Usually the classification algorithms will try to find a point on that axis that differentiate well between the classes or use the value to calculate distance between instances. The salary of an employee is an example of an attribute I will probably use as a numeric attribute.
One more thing you need to take into account is how the classification algorithm treats nominal and numeric attributes. Some algorithms don't handle well nominal attributes. Other algorithms will not work well with several numeric attributes if the values of the attributes are not normalized.