Performance Analysis of Machine Learning Methods
Bibliographic record
Abstract
Abstract Machine Learning has been studied worldwide for its functions in data science and artificial intelligence (AI) fields. Previous works have shown the excellent performance of machine learning methods in image classification. This paper uses various machine learning methods for fashion product classification. This paper aims to analyze the result of predictions for all classes and the first three ranked classes, and meanwhile, compare and discuss Support Vector Machine (SVM), K Nearest Neighbor (KNN), Convolution Neural Network (CNN), Contrastive Language-Image Pre-training (CLIP) methods’ performance. The results show that the F-Score is increased if just predicted for the first three ranked classes, and among SVM, KNN, and CNN models, CNN is the best in both conditions. From the performance of all four models, CLIP was the best model with better learning ability. Besides, the results suggest that an imbalanced dataset may harm predictions, and the CLIP method yields the best result. In the future, CLIP methods may be more likely recommended in an image classification problem with lots of classes, and an imbalanced dataset adjusted will provide new insights into unsolved and unimproved classification problems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".