151. Comparative Analysis of Different Machine Learning Techniques
- Author
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Tripti Lamba, Manisha Agarwal, and Nidhi Srivastava
- Subjects
Data set ,Java ,Mean squared error ,Computer science ,business.industry ,Feature selection ,Artificial intelligence ,business ,Machine learning ,computer.software_genre ,computer ,Random forest ,computer.programming_language - Abstract
Artificial learning (AI) is one of the areas of computer science which develops the system in a way that our system starts learning and gives the reaction as a human brain does. This paper discussed one of the branches of AI i.e. Machine learning as the name suggests it focuses on the development of the computers especially their programs that access the data that can be used for self-development. The main objective of the paper is to compare the best machine learning model by using the performance parameter R squared and Mean square Error (MSE). The data set was taken from the promise repository for the analysis. Feature selection technique Boruta was applied to find the important/confirmed variables from the dataset which are having information regarding the JAVA projects. This paper finds out that among different algorithms which one outperforms when the comparative analysis was done to find the best model.
- Published
- 2020
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