Abstract
A regression tree is one of the most popular machine learning-based decision models. Unlike a decision tree it predicts a continuous value. Many regression models have emerged to handle the regression problems, where most of them faced difficulties while capturing the non-linear patterns. Some regression models are sensitive to outliers, like regression trees. In this paper, a hybrid regression model is proposed, which combines the features of regression tree and ridge regression to improve the performance of regression problem. In the proposed model, the leaf nodes of the regression tree are modified. Rather than storing the mean of the corresponding targeted output values, the proposed hybrid model stores the suitable tuples in its leaf nodes. When some predictor values are inserted, the control transfers to the corresponding leaf node, and ridge regression is applied to the leaf node to predict the required values. In this method, the threshold value plays a vital role in deciding the number of tuples in the leaf nodes, which further affects the time complexity and mean squared error. Extensive comparative analysis has been made by comparing the performance of the proposed model with other regression models using four real-world datasets. The experimental results show that the proposed method outperforms the regression tree and ridge regression when applied individually.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.