Improving prediction efficiency by revolutionary machine learning models
| Autor: | A Abdul Rasheed |
|---|---|
| Rok vydání: | 2023 |
| Předmět: |
010302 applied physics
Mean squared error business.industry Deep learning Process (computing) Mean absolute error 02 engineering and technology General Medicine Extension (predicate logic) 021001 nanoscience & nanotechnology Machine learning computer.software_genre 01 natural sciences Proof of concept 0103 physical sciences Artificial intelligence 0210 nano-technology business computer Mathematics |
| Zdroj: | Materials Today: Proceedings. 81:577-583 |
| ISSN: | 2214-7853 |
| Popis: | Deep learning is the extension of machine learning technique which attracted the researchers in the recent past and hence the literature is also limited. This research focused on the need for deep learning for effective prediction process, when compared with few other machine learning models. As a proof of concept, a dataset of students’ performance of a school is analysed by various machine learning models. The performance measurements by all such models are compared with deep learning. These are assessed by two familiar statistical measures, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The error rates are low for deep learning when compared with other models. It is been evident that the result obtained by the deep learning model is more effective than the other machine learning models. The results are improved by reducing the error rates at the rates of 35% by mean absolute error and 48% by root mean squared error. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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