[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121638-en":3,"doc-seo-121638-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121638,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","A machine learning model for predicting innovation effort of firms - CART","CART models are used to predict research and development intensity, or innovation effort, of firms by leveraging variables such as technical opportunity, knowledge spillover, and absorptive capacity. Results show that CART achieves higher predictive accuracy than commonly used linear parametric models. The study supports data-driven prototypes for helping employees’ innovation thinking be understood, while also providing evidence-based tools for policymakers and business practitioners in decision-making.","A machine learning model for predicting innovation effort of  \nfirms  \nRuchi Rani1, Sumit Kumar2, Rutuja Rajendra Patil2, Sanjeev Kumar Pippal3  \n1Department of Computer Science Engineering, Indian Institute of Information Technology, Kottayam, India 2Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India 3Department of Technology, Nath School of Business and Technology, Mahatma Gandhi Mission University, Aurangabad, India  \nArticle history:  \nReceived Sep 1, 2022 Revised Dec 24, 2022 Accepted Feb 3, 2023  \nKeywords:  \nClassification and regression tree  \nData mining Innovation  \nInnovation predictors Machine learning  \nCorresponding Author:  \nClassification and regression tree (CART) data mining models have been used in several scientific fields for building efficient and accurate predictive models. Some of the application areas are prediction of disease, targeted marketing, and fraud detection. In this paper we use CART which widely used machine learning technique for predicting research and development (R&D) intensity or innovation effort of firms using several relevant variables like technical opportunity, knowledge spillover and absorptive capacity. We found that accuracy of CART models is superior to the often-used linear parametric models. The results of this study are considered necessary for both financial analysts and practitioners. In the case of financial analysts, it establishes the power of data-driven prototypes to understand the innovation thinking of employees, whereas in the case of policymakers or business entrepreneurs, who can take advantage of evidence-based tools in the decision-making process.  \nThis is an open access article under the CC BY-SA license.  \nSumit Kumar  \nDepartment of Electronics and Telecommunication, Symbiosis Institute of Technology, Symbiosis International (Deemed University)  \nPune, Maharashtra, India  \nEmail: [er.sumitkumar21@gmail.com](er.sumitkumar21@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nResearch and development (R&D) are one of the most critical issues presently upsetting the growth of entrepreneurs. Over the past decade, a modern production factor has developed a fundamental cause of competitive advantage-knowledge, learning, and ingenuity. All these aspects certainly lead to increased innovation activity and the creation of innovations. This is feasible to attain by engaging monetary bodies in cooperative chains. So, the so-called knowledge spillover [1] effects become a side effect of any defined cooperation with a knowledge base. Smart cities present a highly conducive environment for innovative and user-friendly innovation and can allocate resources to develop new urban and regional innovation systems.  \nClassification and regression trees (CART) are a family of machine learning techniques popular in several scientific fields that do not assume data normality and user-specified model statements like ordinary least square (OLS) regression. These methods are easy to use and interpret. Regression trees are non-parametric and computationally intensive methods. They can be applied to a large dataset with high dimensions and are resistant to outliers [2] . These methods are helpful even if someone plans to use conventional methods for identifying the essential variables if there are many variables.  \nSome important algorithms for tree-based regressions are automatic interaction detection (AID), Chi-squared automatic interaction detection (CHAID), CART, and C 5.0. as numerous implementations of these algorithms exist due to a very active machine learning community. In this paper we will discuss only  \nthe CART algorithm followed by an application on a business performance and enterprise survey (BEEPs) dataset from German industry, 2005. Nonlinear innovation models like CART have recently been introduced to accommodate interactive and recursive concepts. However, little attention has been paid to predicting innovation activity us","cbCaioAOhdo7YAZv","https://ap.wps.com/l/cbCaioAOhdo7YAZv","pdf",402503,1,7,"English","en",105,"# Keywords\n# Introduction\n## Role of R&D and innovation\n## CART approach and motivation\n## Tree-based algorithms and gap in nonlinear innovation prediction\n# Method and dataset\n## BEEPs German industry data\n## Modeling with rpart in R\n# Results and interpretation\n## Error vs. number of splits and pruning\n## Comparing linear vs. tree predictions\n## Variable importance and knowledge spillovers","[{\"question\":\"What is the goal of the paper?\",\"answer\":\"To predict firms’ innovation effort (R\\u0026D intensity) using a machine learning approach based on CART and relevant explanatory variables.\"},{\"question\":\"Which variables are used to predict innovation effort?\",\"answer\":\"The paper uses factors such as technical opportunity, knowledge spillover, and absorptive capacity.\"},{\"question\":\"How does CART performance compare with linear parametric models?\",\"answer\":\"CART models provide superior accuracy for predictive purposes compared with the often-used linear parametric models.\"}]","A machine learning model for predicting innovation effort of firms - 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