<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, firstly, the social network theory and features are studied in detail, and the key to improving the model prediction performance using the Bagging algorithm is to use the CART regression tree algorithm to make the variance as small as possible while ensuring that the average performance of the model does not drop significantly. Next, the random forest model was improved by using the feature importance measure algorithm and the F-measure weighting algorithm, which improved the overall performance of the random forest model. Then the elements of innovation and entrepreneurship ability of college students were identified, and the basic characteristics of the research subjects were obtained using the scale, and the influence of social networks on innovation and entrepreneurship ability based on the random forest model was studied empirically. The results show that the coefficients of the two dimensions of association strength and heterogeneity in the dimensions of social networks of college students are larger, 0.251 and 0.222, respectively, indicating that it is the network association strength and heterogeneity that have a greater impact on the improvement of innovative thinking ability. This study promotes innovation and entrepreneurship education in colleges and universities, which is important for the improvement of students’ innovation and entrepreneurship ability.</p>
</abstract>

In this paper, firstly, the social network theory and features are studied in detail, and the key to improving the model prediction performance using the Bagging algorithm is to use the CART regression tree algorithm to make the variance as small as possible while ensuring that the average performance of the model does not drop significantly. Next, the random forest model was improved by using the feature importance measure algorithm and the F-measure weighting algorithm, which improved the overall performance of the random forest model. Then the elements of innovation and entrepreneurship ability of college students were identified, and the basic characteristics of the research subjects were obtained using the scale, and the influence of social networks on innovation and entrepreneurship ability based on the random forest model was studied empirically. The results show that the coefficients of the two dimensions of association strength and heterogeneity in the dimensions of social networks of college students are larger, 0.251 and 0.222, respectively, indicating that it is the network association strength and heterogeneity that have a greater impact on the improvement of innovative thinking ability. This study promotes innovation and entrepreneurship education in colleges and universities, which is important for the improvement of students’ innovation and entrepreneurship ability.

Research on the influence of social network on young people’s capacity of innovation and entrepreneurship based on stochastic forest model

Nanjing University of Science and Technology

Applied Mathematics and Nonlinear Sciences

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

In this paper, firstly, the social network theory and features are studied in detail, and the key to improving the model prediction performance using the...

Research on the influence of social network on young people’s capacity of innovation and entrepreneurship based on stochastic forest model

Publicado en línea: 11 oct 2023

Páginas: -

Recibido: 16 dic 2022

Aceptado: 21 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00611

Palabras clave
Bagging algorithm, CART regression tree algorithm, F-measure weighting algorithm, Random forest model, Social network

© 2023 Kang Yu et al., published by Sciendo

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

Research on the influence of social network on young people’s capacity of innovation and entrepreneurship based on stochastic forest model

Publicado en línea: 11 oct 2023

Páginas: -

Recibido: 16 dic 2022

Aceptado: 21 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00611

Palabras claveBagging algorithm, CART regression tree algorithm, F-measure weighting algorithm, Random forest model, Social network

© 2023 Kang Yu et al., published by Sciendo

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

Palabras clave
Bagging algorithm, CART regression tree algorithm, F-measure weighting algorithm, Random forest model, Social network