Importance, Advantages, and Disadvantages of Using Decision Trees for Data Analysis techwestnews.com
Another major importance of decision trees lies in the phase of data preparation learning models. According to the decision tree models, there is less applicability of data clearance as compared to other data analysis approaches. These always avoid the data normalization requirement in data analysis early phases. Decision tree models are applicable in both numerical and categorical data sets, so in qualitative variables, there is no need for transformation as required in other data analysis techniques. Following are the major significances of decision trees in data analysis:
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