Mobile Phone Price Prediction with Feature Reduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Feature reduction can reduce data dimensionality and streamline model size, which focuses on the high relevance data and inferences the output faster. This paper aims to explore the performance and effectiveness of feature reduction methods that accompany the Multilayer Perceptron classifier in predicting the mobile phone price range. Pearson’s Correlation and Principal Components Analysis are chosen as the feature reduction techniques in the research. The experiment sorts the features in significant order with two distinct methods. The three experimental groups reduce 5 features each time and the control group has no feature selection. Then all the groups use the open dataset to train and test the accuracy and loss through MLP. The result indicates that the feature selected by the correlation coefficient facilitates the accuracy of the classification model. When PCA is implemented and only a few features get reduced, the performance improves a little bit, but when more features are eliminated there are huge negative influences. Pearson’s correlation has a better performance than PCA in this experiment, which achieves 95.8% accuracy and validate the effectiveness of the feature reduction method.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it