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<p>Based on the lack of management level and other problems in enterprise management innovation, this paper analyzes the innovation pathway of enterprise management by logistic regression algorithm. Firstly, the management pathway is set as a data set, and the feature vector of the model is defined, the probability model of logistic regression is derived from the input data vector, and the model defines the likelihood probability of the training set. Then the log-likelihood function of the logistic regression model with beta distribution is calculated, the feature weights of the data set are obtained using this function, and finally, the effective countermeasures of enterprise management innovation pathways under logistic regression are obtained according to these weights. The results show that by examining the variance comparison of different enterprise properties, private and sole proprietorship enterprises have the highest scores in the external organizational management factors, 3.86 and 3.72, respectively. The factors in the strategic technological capability have the lowest scores compared with other factors. It can be seen that the construction of comprehensive innovation management capability of enterprises is a long-term accumulation process, and the logistic regression algorithm helps enterprises to develop feasible innovation management pathway strategies according to their capabilities.</p>
</abstract>

Based on the lack of management level and other problems in enterprise management innovation, this paper analyzes the innovation pathway of enterprise management by logistic regression algorithm. Firstly, the management pathway is set as a data set, and the feature vector of the model is defined, the probability model of logistic regression is derived from the input data vector, and the model defines the likelihood probability of the training set. Then the log-likelihood function of the logistic regression model with beta distribution is calculated, the feature weights of the data set are obtained using this function, and finally, the effective countermeasures of enterprise management innovation pathways under logistic regression are obtained according to these weights. The results show that by examining the variance comparison of different enterprise properties, private and sole proprietorship enterprises have the highest scores in the external organizational management factors, 3.86 and 3.72, respectively. The factors in the strategic technological capability have the lowest scores compared with other factors. It can be seen that the construction of comprehensive innovation management capability of enterprises is a long-term accumulation process, and the logistic regression algorithm helps enterprises to develop feasible innovation management pathway strategies according to their capabilities.

Analysis and elaboration of enterprise management innovation path based on logistic regression perspective

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Based on the lack of management level and other problems in enterprise management innovation, this paper analyzes the innovation pathway of enterprise...

Analysis and elaboration of enterprise management innovation path based on logistic regression perspective

Publié en ligne: 28 avr. 2023

Pages: 1387 - 1400

Reçu: 13 avr. 2022

Accepté: 15 juil. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00042

Mots clés
Logistic regression algorithm, Dataset, feature weights, Beta distribution, Likelihood function

© 2023 Jia Zhao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis and elaboration of enterprise management innovation path based on logistic regression perspective

Publié en ligne: 28 avr. 2023

Pages: 1387 - 1400

Reçu: 13 avr. 2022

Accepté: 15 juil. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00042

Mots clésLogistic regression algorithm, Dataset, feature weights, Beta distribution, Likelihood function

© 2023 Jia Zhao, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Logistic regression algorithm, Dataset, feature weights, Beta distribution, Likelihood function