In today’s class, I’ve learnt about decision trees. Decision trees are essentially a graphical representation of decision-making processes. Think of them as a series of questions and choices that lead to a final conclusion. At the tree’s outset, you encounter the initial question, and as you answer each question, you progress down the branches until you arrive at the ultimate decision.
The construction of a decision tree entails selecting the most informative questions to ask at each juncture. These questions are based on various attributes or features of the data, and their selection is guided by statistical measures like information gain, Gini impurity, or entropy. The goal is to optimize the decision-making process by selecting the most relevant attributes at each node.
However, decision trees have limitations, especially in scenarios where the data exhibits a wide spread or deviation from the mean. In our recent Project 2, we encountered a dataset in which the mean was considerably distant from the majority of data points, making the decision tree method less efficient. This highlights the importance of considering the distribution and characteristics of the data when choosing the appropriate statistical method for analysis. Decision trees are a valuable tool, but their efficacy is contingent on the nature of the data they are applied to, and sometimes alternative statistical methods may be more suitable for handling such situations.