Comparative Study using Dissimilar Training Sets on a Random Forest Algorithm
One of the best methods for prediction is random forest that has gained popularity due to its performance and training efficiency. This paper presents an experimental comparison of a supervised learning model applied on a heart disease dataset, but with dissimilar training sets. This paper claims that updatable learning is required when data is updated frequently. It also discusses the number of issues that need to be resolved when information is updated periodically.