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DAT 520 Problem Set 4

A decision tree is defined as a decision support tool which uses a model which is tree-like to come up with decisions and their relevant consequences. The tree s represented graphically to provide possible solutions to a given decision as per specific conditions. There are many reasons that explain why a decision tree has to be used. In machine learning, a decision tree is a supervised technique where the input data and the corresponding output data are known and hence used in the training where the data is split continuously as per specific parameters. Consequently, the two ways in which decision trees are used is when the possible consequences of a decision are to be explored and secondly, when the values of outcomes need to be quantified and the probabilities of attaining them be stated. The first tool to make the decision tree is the use of excel Decision tree and secondly, decision trees can as well be made using word using the shape library or the SmartArt.

Using decision trees is excel is easier to read and interpret and secondly, there is less data cleaning that is required when using the tools in excel. Furthermore, the outputs are easier to read and interpret with minimal statistical knowledge. Word is also easier to implement due to the many shape files that are present in the latest version of office word.

While using both the word and excel tool in making the decision tree, notable challenges may arise. For instance, a small change in the decision tree structure may result to a different result which may not be captured easily using word or excel. Unlike machine learning tools such as R, word and excel are manual and slow to implement.