Predicting consumer acceptance of automobiles based on deep learning and traditional machine learning algorithms
Bibliographic record
Abstract
Researchers have made significant progress in machine learning in recent years. Machine learning can learn and predict large and complex data sets. Researchers have divided machine learning algorithms into two categories: deep learning and traditional machine learning. Every problem can be predicted in both ways. This paper uses the "Car Data" dataset to investigate deep learning and traditional machine learning. In order to find a machine learning algorithm that is more conducive to analyzing and predicting consumers' acceptance of different cars, this paper mainly explores the differences in the prediction accuracy of the three methods of Neural Networks, Random Forest and Support Vector Machine (SVM). We construct 3-hidden layers neural networks and 4-hidden layers neural networks. After testing, it is known that the result predicted by Random Forest is the worst. The prediction accuracy of 3-hidden layers Neural Networks is similar to that by SVM. When we added an extra layer of hidden layers on the basis of 3-hidden layers, the prediction accuracy was higher than that of SVM. Adding a hidden layer can improve the prediction accuracy, and both SVM and Neural Network can be used to analyze Car Data. But not all methods have similar predictive accuracy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".