ABSTRACT
The machine learning algorithms work accurately with the appropriate instances and their attributes. We all know the instances are the real data world value, which is gathered from the real world itself. So the instance cannot be created, cannot be modified nor destroyed. It is important to select the appropriate attributes. As seen in the real datasets, the deeper you go, the more difficult it is to find appropriate attributes. This is because of the reason that the attributes are smaller in size. To deal with such an issue, it is necessary to know how well the attributes behave in the machine learning models. Sometimes, it is challenging to filter the relevant attributes. This is the case when the filtered attributes are less in number and strong dependency gets created. The target attribute then seems to adopt the values from the attribute with the same nominal range or learns exactly the similar patterns of the data.
