In machine learning, there has been a shift away from focusing on the creation complex algorithms to solve problems. Instead, a large focus has been on the application of simpler algorithms which learn from datasets. This shift has been made possible through the ever-increasing computational power of modern computers, and the massive amounts of data generated and gathered through the internet of things.\u00a0However, even given the power and storage cheaply available for creating these models, it can still be quite time and space intensive to make a useful machine learning model. Datasets can vary in size, but can range from hundreds of thousands, millions, or even billions of unique data points. Due to the copious amount of data, training even a relative fast machine learning model can take hours or days. Because of how time and resource intensive that process is, companies often wait to recreate the model, even though they may lose some performance due to it. Some companies even wait to do it on a monthly basis, such as CoverMyMeds, who update their models every 28 days.<\/p>\n
Part of why updating models is so intensive is that many do not allow data to be added after they are initially trained. This means each time you want to add data, you must create a new version from scratch, using the old set and the new points. Other types of models do allows this though, so it is possible to add it. The aim of my research focuses on learning how to add data dynamically to the model from neural networks, a machine learning algorithm based off of how the brain works with neurons, and apply similar logic to classification decision trees. The hypothesis of my research is that the time intensity of updating a decision tree can be decreased by adding data incrementally, with little loss to the tree’s effectiveness.<\/p>\n