Meta random forest : random forest with simple random forest as base model
The abundance of data along with the significant rise of computational power boost the popularity and usage of machine learning in recent decision making and forecasting. Random forest, being one of the current state of the art model, are well known of its high accuracy and efficiency for both class...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/144849 |
| _version_ | 1811697288522760192 |
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| author | Kurniawan, Billy |
| author2 | Pan Guangming |
| author_facet | Pan Guangming Kurniawan, Billy |
| author_sort | Kurniawan, Billy |
| collection | NTU |
| description | The abundance of data along with the significant rise of computational power boost the popularity and usage of machine learning in recent decision making and forecasting. Random forest, being one of the current state of the art model, are well known of its high accuracy and efficiency for both classification and regression problems.
In this paper, Meta Random Forest is introduced to the random forest method to make it even more accurate both in classification and regression problems. Shortly, meta random forest is a method where simple random forests are used as the base for the main random forest. To compare the performance of Meta Random Forest, we use accuracy score for classification problems and R2 score for regression problems. |
| first_indexed | 2024-10-01T07:52:53Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/144849 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:52:53Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1448492023-02-28T23:18:11Z Meta random forest : random forest with simple random forest as base model Kurniawan, Billy Pan Guangming School of Physical and Mathematical Sciences GMPAN@ntu.edu.sg Science::Mathematics The abundance of data along with the significant rise of computational power boost the popularity and usage of machine learning in recent decision making and forecasting. Random forest, being one of the current state of the art model, are well known of its high accuracy and efficiency for both classification and regression problems. In this paper, Meta Random Forest is introduced to the random forest method to make it even more accurate both in classification and regression problems. Shortly, meta random forest is a method where simple random forests are used as the base for the main random forest. To compare the performance of Meta Random Forest, we use accuracy score for classification problems and R2 score for regression problems. Bachelor of Science in Mathematical Sciences 2020-11-30T06:24:57Z 2020-11-30T06:24:57Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/144849 en application/pdf Nanyang Technological University |
| spellingShingle | Science::Mathematics Kurniawan, Billy Meta random forest : random forest with simple random forest as base model |
| title | Meta random forest : random forest with simple random forest as base model |
| title_full | Meta random forest : random forest with simple random forest as base model |
| title_fullStr | Meta random forest : random forest with simple random forest as base model |
| title_full_unstemmed | Meta random forest : random forest with simple random forest as base model |
| title_short | Meta random forest : random forest with simple random forest as base model |
| title_sort | meta random forest random forest with simple random forest as base model |
| topic | Science::Mathematics |
| url | https://hdl.handle.net/10356/144849 |
| work_keys_str_mv | AT kurniawanbilly metarandomforestrandomforestwithsimplerandomforestasbasemodel |