Comparing Supervised Machine Learning Algorithms on Classification Efficiency of multiclass classifications problem
Bibliographic record
Abstract
Multi-class classification is a fascinating field to study. However, evaluating the classification performance of classifiers is difficult. Class indices such as accuracy, precision, recall, and F-measure, Kappa and area under the curve of receiver operating characteristics (AUC), can be used to evaluate classification performance. These indices describe the classification results achieved on each modelled class. Several measures have been introduced in the literature to deal with this assessment, the most commonly used being accuracy. In general these metrics were proposed to address binary classification tasks, whereas multiclass classification is the more difficult and currently active research area in machine learning (ML). In this paper, we intended to compare classification performance of nine supervised machine learning algorithms based on three learner types: statistical learner, rule-based learner and neural-base learner by considering accuracy, precision, recall and F-measure and ROC area achieved on four different datasets from UCI machine repository. Among these, Random forest has been the best performance in both 10 fold cross validation and percentage split with overall average accuracy of predictive power of 92.20% and 91.76% respectively, with less variability, whereas Naïve Bayes has the worst also in both 10 fold cross validation and percentage split by average correct classification performance of 79.18% and 76.92% respectively, and also with higher variability next to Decision Table.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".