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Decision tree mostly used in forecasting or predictive modelling. It is widely used in the data science for getting the relevant outcome. Mostly used in machine learning, data mining and developing the predictive model. Decision tree is a graph to represent choices and their results in form of a tree. The nodes in the graph represent an event or choice and the edges of the graph represent the decision rules or conditions. This is used in real data analysis so many examples are there /here listed few examples

  1. Predicting the loan—- bad or good credit/also known as risk management system
  2. Predicting the email—– spasm or not spasm
  3. Predicting the cancer——- carcinogenic/not carcinogenic

So many examples are there, for coming to the end point lot of vigorous validation is required for fitting the model coz accuracy is matter. For decision tree required lot of training data’s or observed data. This can also determine by SAS by using the regression/multiple linear/logistic regression model. Even we are also able to determine by power BI visual tools that is known as decomposition tree visual.

The R package “party” is used to create decision trees. We will use the ctree() function to create the decision tree and see its graph.

Decision Tree in R Programming

Decision Trees are a well-known Data Mining approach that employs a tree-like structure to offer outcomes based on input decisions. If you’re looking for a Power BI Online Training course near me, look no further. If so, Durga Online Trainer is the greatest training institute for learning comprehensive Data Science, SAS, and Power BI at a very low course charge.