8th Nov 2023

In today’s class, we explored the concept of decision trees, which are graphical structures used to map out decision-making processes. Decision trees are built by repeatedly dividing datasets based on chosen features to improve decision-making. The process begins with the crucial step of feature selection, where metrics like information gain, Gini impurity, or entropy guide the choice. Then, the algorithm applies specific criteria, like Gini impurity for classification or mean squared error for regression, to segment the data until a stopping condition is met. However, it’s essential to acknowledge that decision trees have their limitations, especially when dealing with data that significantly deviates from the mean. Recent project experiences have demonstrated that decision trees can be less effective in such scenarios, emphasizing the need to carefully consider the data’s unique characteristics when selecting the most suitable method. Therefore, while decision trees are valuable tools, their performance hinges on the specific characteristics of the data, and in certain situations, alternative methods may be more appropriate.