Random Forest Regression for Sailboat Price Prediction: A Methodological Approach

A sample of size N is randomly drawn with replacement N times, with one sample drawn each time. The chosen N samples are used to train a decision tree, which serves as the root node of the tree. Each sample contains M attributes, and when a node in the tree needs to be split, m attributes are randomly selected from the M attributes, where m << M. Then, using a certain strategy, one attribute is chosen from the m attributes as the splitting attribute for that node. This process of splitting continues at each node until no further splitting is possible. By constructing a large number of decision trees, a random forest is created to fit a regression model for predicting sailboat prices based on the aforementioned influencing factors. The steps involved in this process are illustrated in the diagram below.