Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Proceedings of the International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2022)
Series Vol. 2 , 20 February 2023
Open
Access | Article
Python-based Model Optimization Platform
* Author to whom correspondence should be addressed.
Abstract
With the increasing degree of informatization in today's society, the presentation of problems has become more complex, which puts forward higher requirements for people's ability to solve problems. Python is a popular language recently, and it is very popular among developers because of the many mature libraries that are encapsulated in it. People can use related libraries in Python and use open-source related libraries for algorithm research. The main purpose of this paper is to study the optimization platform of the model based on Python. This paper mainly analyzes the characteristics of the Python language and the structure of Python programming, and uses the relevant database of Python to realize the modeling work. The experiment shows that the accuracy of the decision tree model is 96.94 %, the accuracy of the KNN classification model is 89.05%.
Keywords
Python Programs, Python Language, Database Sets, Model Optimization
References
1. Rao C S , Karunakara K . Efficient Detection and Classification of Brain Tumor using Kernel based SVM for MRI[J]. Multimedia Tools and Applications, 2022, 81(5):7393-7417.
2. Alonso D H , Rodriguez L , Silva E . Flexible framework for fluid topology optimization with OpenFOAMand finite element-based high-level discrete adjoint method (FEniCS/dolfin-adjoint)[J]. Structural and Multidisciplinary Optimization, 2021, 64(6):4409-4440.
3. Farina F , Camisa A , Testa A , et al. DISROPT: a Python Framework for Distributed Optimization[J]. IFAC-PapersOnLine, 2020, 53(2):2666-2671.
4. Ullah A , Wang B , Sheng J , et al. Optimization of software cost estimation model based on biogeography-based optimization algorithm[J]. Intelligent Decision Technologies, 2020, 14(4):1-8.
5. Jammalamadaka K , Parveen N . Testing coverage criteria for optimized deep belief network with search and rescue[J]. Journal of Big Data, 2021, 8(1):1-20.
6. Cragnolini T , Sahota H , Joseph A P , et al. TEMPy 2: a Python library with improved 3D electron microscopy density-fitting and validation workflows[J]. Acta Crystallographica Section D Structural Biology, 2021, 77(1):41-47.
7. R Sreenivasulu, Chaitanya G , Kumar G V , et al. Inverse Kinematic Solution for Five bar Parallel Linkage Planar Manipulator using PYTHON and Optimization by Taguchi Method[J]. International Journal of Engineering Trends and Technology, 2021, 69(5):94-100.
8. Rao A , Rao C S , Cheruku D R . Differentiating digital image forensics and tampering localization by a novel hybrid approach[J]. Multimedia Tools and Applications, 2022, 81(13):18693-18713.
9. Minquiz G M , Borja V , M López-Parra, et al. Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations[J]. Mathematical Problems in Engineering, 2020, 2020(5):1-13.
10. Fearn S , Trembath D . Southern distribution limits and a translocated population of the scrub python Morelia kinghorni (Serpentes: Pythonidae) in tropical Queensland[J]. Herpetofauna, 2020, 36(2):85-87.
11. Rao C S , Karunakara K . Efficient Detection and Classification of Brain Tumor using Kernel based SVM for MRI[J]. Multimedia Tools and Applications, 2022, 81(5):7393-7417.
12. Melingi S B , Mojjada R K , Tamizhselvan C , et al. A self-adaptive monarch butterfly optimization (MBO) algorithm based improved deep forest neural network model for detecting and classifying brain stroke lesions[J]. Research on Biomedical Engineering, 2022, 38(2):647-660.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2022)
- ISBN (Print)
- 978-1-915371-13-3
- ISBN (Online)
- 978-1-915371-14-0
- Published Date
- 20 February 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/2/20220183
- Copyright
- © 2023 The Author(s)
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited