Explaining machine learning models in sales predictions

Bohanec, Marko; Borštnar, Mirjana Kljajić; Robnik–Šikonja, Marko

doi:10.1016/j.eswa.2016.11.010

Cited by 120 publications

(90 citation statements)

References 26 publications

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“…The complete list is described in Bohanec et al (2017aBohanec et al ( , 2017b. A detailed description of attributes identification, analysis of attributes importance, and final attribute selection can be found in (Bohanec et al, 2015a(Bohanec et al, , 2015bBohanec et al, 2016aBohanec et al, , 2016b.…”

Section: Featuresmentioning

confidence: 99%

“…The present paper builds on our previous work, where we proposed a novel use of general explanation methodology inside an intelligent system in a real-world case of B2B sales forecasting (Bohanec et al, 2017a). We first assembled a set of attributes from academic literature (Bohanec, Kljajić Borštnar and Robnik-Šikonja, 2015a), developed an optimization process to define the minimum sized data set (Bohanec, Kljajić Borštnar and Robnik-Šikonja, 2016b) and built machine learning model (Bohanec et al, 2017a).…”

Section: Related Workmentioning

confidence: 99%

“…We first assembled a set of attributes from academic literature (Bohanec, Kljajić Borštnar and Robnik-Šikonja, 2015a), developed an optimization process to define the minimum sized data set (Bohanec, Kljajić Borštnar and Robnik-Šikonja, 2016b) and built machine learning model (Bohanec et al, 2017a). The ML model, enhanced with the general explanation methods, was applied in a real-world B2B process so that users could validate their assumptions with the presented explanations and test their hypotheses using the presented what-if parallel graph representation.…”

Section: Related Workmentioning

confidence: 99%

“…Besides the classification accuracy (CA) measure, we observed also the ROC curve (AUC measure). ML models explanations were generated by IME and EXPLAIN methods (Bohanec et al, 2017a). …”

Section: Crisp Dmmentioning

confidence: 99%

“…The novel use of general explanation methodology applied to business (B2B) sales forecasting process was introduced in Bohanec et al (2017a) in the context of a decision-making framework with double-loop learning (Bohanec et al, 2017b). Analysis of the proposed organizational model and its implementation as a part of research design is discussed in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Organizational Learning Supported by Machine Learning Models Coupled with General Explanation Methods: A Case of B2B Sales Forecasting

Bohanec¹,

Robnik–Šikonja

Borštnar

2017

Organizacija

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Background and Purpose:The process of business to business (B2B) sales forecasting is a complex decision-making process. There are many approaches to support this process, but mainly it is still based on the subjective judgment of a decision-maker. The problem of B2B sales forecasting can be modeled as a classification problem. However, top performing machine learning (ML) models are black boxes and do not support transparent reasoning. The purpose of this research is to develop an organizational model using ML model coupled with general explanation methods. The goal is to support the decision-maker in the process of B2B sales forecasting. Design/Methodology/Approach: Participatory approach of action design research was used to promote acceptance of the model among users. ML model was built following CRISP-DM methodology and utilizes R software environment. Results: ML model was developed in several design cycles involving users. It was evaluated in the company for several months. Results suggest that based on the explanations of the ML model predictions the users' forecasts improved. Furthermore, when the users embrace the proposed ML model and its explanations, they change their initial beliefs, make more accurate B2B sales predictions and detect other features of the process, not included in the ML model. Conclusions:The proposed model promotes understanding, foster debate and validation of existing beliefs, and thus contributes to single and double-loop learning. Active participation of the users in the process of development, validation, and implementation has shown to be beneficial in creating trust and promotes acceptance in practice.

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Section: Featuresmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Besides the classification accuracy (CA) measure, we observed also the ROC curve (AUC measure). ML models explanations were generated by IME and EXPLAIN methods (Bohanec et al, 2017a). …”

Section: Crisp Dmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Organizational Learning Supported by Machine Learning Models Coupled with General Explanation Methods: A Case of B2B Sales Forecasting

Bohanec¹,

Robnik–Šikonja

Borštnar

2017

Organizacija

Self Cite

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Client Management

2020

Artificial Intelligence for Audit, Forensic Accounting, and Valuation

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Blockchain for explainable and trustworthy artificial intelligence

Nassar

Salah

Rehman

et al. 2019

WIREs Data Min & Knowl

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The increasing computational power and proliferation of big data are now empowering Artificial Intelligence (AI) to achieve massive adoption and applicability in many fields. The lack of explanation when it comes to the decisions made by today's AI algorithms is a major drawback in critical decision‐making systems. For example, deep learning does not offer control or reasoning over its internal processes or outputs. More importantly, current black‐box AI implementations are subject to bias and adversarial attacks that may poison the learning or the inference processes. Explainable AI (XAI) is a new trend of AI algorithms that provide explanations of their AI decisions. In this paper, we propose a framework for achieving a more trustworthy and XAI by leveraging features of blockchain, smart contracts, trusted oracles, and decentralized storage. We specify a framework for complex AI systems in which the decision outcomes are reached based on decentralized consensuses of multiple AI and XAI predictors. The paper discusses how our proposed framework can be utilized in key application areas with practical use cases. This article is categorized under: Technologies > Machine Learning Technologies > Computer Architectures for Data Mining Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining

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Explaining machine learning models in sales predictions

Cited by 120 publications

References 26 publications

Organizational Learning Supported by Machine Learning Models Coupled with General Explanation Methods: A Case of B2B Sales Forecasting

Organizational Learning Supported by Machine Learning Models Coupled with General Explanation Methods: A Case of B2B Sales Forecasting

Client Management

Blockchain for explainable and trustworthy artificial intelligence

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