Vaidya, Anil Vishnu

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Association for Information Systems AIS Electronic Library (AISeL) ICIS 2020 TREO Papers 12-14-2020 Multi-out Regression – Finding the right algorithm Anil Vishnu Vaidya S P Jain Institute of Management & Research, anil.vaidya@spjimr.org Follow this and additional works at: https://aisel.aisnet.org/treos_icis2020 Recommended Citation Vaidya, Anil Vishnu, "Multi-out Regression – Finding the right algorithm" (2020). ICIS 2020. 13. https://aisel.aisnet.org/treos_icis2020/13 This material is brought to you by the TREO Papers at AIS Electronic Library (AISeL). It has been accepted for inclusion in ICIS 2020 by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact elibrary@aisnet.org. TREO Technology, Research, Education, Opinion Presentation at TREO Talks in conjunction with the 41s t International Conference on Information Systems, ICIS 2020 TREO Talks are not peer-reviewed and not a formal part of the ICIS 2020 Proceedings All TREO Talks are available in the TREO Talks section of the AIS e-Library Multi-out Regression – Finding the Right Algorithm  Anil Vishnu Vaidya, anil.vaidya@spjimr.org anvvaidya@gmail.com   In the real world there are challenging situations that demand multi-output regression to estimate or predict the multiple outcomes from a given set of datapoints. A characteristic of such a problem is that the outputs are dependent on independent variables but also on each other, increasing complexity of the modelling exercise. Some suggest that in the machine learning parlance such a problem can be approached by modelling each outcome separately. This author tried and presented paper based on method of separate modelling in one of the conferences this year. One of the conference attendee suggested the structured equation modelling. Continuation on machine learning path I decided to explore multi-out regressors offered by various sources. Zhen et al. (2016) have offered a supervised descriptor learning (SDL) algorithm for multi-out regression claiming that it improves performance significantly in various applications. Baigaite et al. (2020) show the ability to model multi output regression with high accuracy for global vegetation even with incomplete data. Getting some clues from the research papers and articles I worked on multiple methods to confirm the method to improve the accuracy. I worked on following two datasets: 1. I had gathered data through an agility survey of multiple companies operating in India. This is about modelling the operational agility and strategic agility as two target variables based on 24 features with 153 datapoints.  2. I also used ‘Energy efficiency dataset’ from UCI machine learning library with 768 datapoints and heating and cooling load as two outcomes  Scikit Learn offers multi target regression in python that allows to estimate multiple outcomes simultaneously. Also Keras offers facilities to model a neural network for multiple continuous outputs. It was interesting to see that in the energy efficiency dataset the MuitiOutRegressor modelled with r2 as 0.89, however predictions using this model did not seem to be to be right.   I propose to model both datasets viz. my own agility dataset and the Energy Efficiency dataset using multiple multiout regression algorithms available in sklearn and in keras. Since the datasets are not large enough I will be using k-fold validation for training and testing. I would like to find the algorithm that will work in all situations and context, producing reliable result.  References Baigaite, R., Read, J. and Zliobaite, I. (2020) Multi-output prediction of global vegetation distribution with incomplete data 37th International Conference on Machine Learning (ICML)  Rai, P., Kumar, A. and Daume, H. (2012) Simultaneously leveraging output and task structures for multiple-output regression Neural Information Processing Systems Conference Proceedings   Zhen, X., Yu, M., Islam, A., Bhaduri, M., Chan, I. and Li, S. (2016) Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression IEEE Transactions on Neural Networks and Learning Systems   

Multi-out Regression – Finding the right algorithm

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Multi-out Regression – Finding the right algorithm

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