Optimized Random Forest Classifier for Predicting Ideal Candidate for The General Election
Bibliographic record
Abstract
Electoral systems and candidate selections are the two important pillars of modern democratic elections. The integrity and competence of candidates are significant contributing factors to government enactment. Hence, the selection of competent candidates is an indispensable process in the electoral system. This research proposes a new model, named RANWIN (Random forest classifier-based model for selecting Winning candidates), for predicting the winning probability of the candidate and the party. Our model integrates Random Forest Classifier (RFC) and Bayesian Optimization Technique (BOT) to achieve outstanding performance. It considers several factors for predicting the winning probability of candidates, including the popularity of the candidate, past election history, party change or personalization, vote bank, etc. For predicting the winning probability of the party, this method considers the manifestoes, vote bank, leaders, electoral history, intraparty struggles, major rallies hosted in the constituency, and the strength of alliance parties etc. After calculating the impact of all these parameters, RANWIN uses RFC to predict the winning possibility of the candidates as well as parties. To enhance the efficiency of the intended framework, we apply BOT in the prevailing RFC to find out the optimal hyperparameters of the classification process. To prove the better performance of our framework, its enactment is related to other existing approaches regarding accuracy, precision, the Area Under the receiver operating Characteristic (AUC) curve, recall, F-measure, and Root Mean Square Error (RMSE). The empirical results demonstrate that RANWIN can be considered as a more effective method with higher predictive accuracy, precision, AUC, recall, F-measure, and lower RMSE of 94.32%, 95.5%, 90.6%, 97.6%, 98.0%, and 16.23%, correspondingly. As a result, we can prove that RANWIN is an improved framework for candidate selection and has a positive impact on the current political landscape as related to approaches based on other machine learning techniques. The key goal of this research is to help political parties select the right candidates to win public office.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".