Prediction of the age of abalones based on machine learning algorithms

Abstract

Abalone is an important seafood, widely used in food, medicine, and other fields. The age of abalone is one of the important factors that determine its quality and market value. However, the traditional age determination method requires the dissection of abalone, which is time-consuming and expensive. Therefore, it is important to find a fast and work out age prediction method. This article uses a machine learning algorithm to predict the age of abalone. The authors collected data on characteristics such as sex, length, diameter, height, and weight for 4177 abalone observations. This data set is admirably large. In the following study, the authors compare the effects of prediction using different machine learning algorithms, including linear regression, decision trees, random forests, and support vector machines. It is worth mentioning that the authors have done sufficient research and evaluation of these algorithms to find out the best prediction scheme. The results show that the random forest algorithm is the best, and its average absolute error is only 1.44 years. The performance of random forest algorithm is the best.