A Comparative Study of Regression Machine Learning Algorithms: Tradeoff Between Accuracy and Computational Complexity
Bibliographic record
Abstract
The computational complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers which is the determination of looking for the best methods to solve a problem. The accuracy of a regression model's predictions must be reported as an error. According to the researchers, the most problematic issue is the lack of a properly defined machine learning assessment. In this research, Various types of machine learning regression algorithms, namely, Linear Regression, Support Vector Regression, Random Forest Regression, and Multilayer Perceptron Neural Network have been used to process and analyze the collected data in terms of comparison of their accuracy and the computational complexity. The applied dataset was collected using IoT sensors seeking an appropriate algorithm that is the fittest to the collected data to design a model system that represents the goal of specific future applications. The result shows that the Random Forest regression has the highest computational complexity and highest accuracy depending on the calculated error metrics (Mean Square Error, Mean Absolute Error, and R Squared score) which are (0.0002, 0.005, and 0.995) respectively. Based on that, Random Forest Regression will be adapted and implemented with the structure of a planned design system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".