Analysis of the Price Influence Factors of Used Audi Cars Based on Ridge Regression Model
Bibliographic record
Abstract
This paper uses the ridge regression model to explore the factors affecting the price of second-hand Audi cars. A large number of used Audi car feature data were collected, including the Model, Year, Mileage and other characteristics, as well as their corresponding price. In general, since the development of these factors is homogeneous, so most of their data have multicollinearity problems. If OLS is used to estimate the parameters of the model, the parameters obtained may be difficult to objectively and accurately reflect the actual situation [6]. Using ridge regression model for modeling and prediction to solve the multicollinearity problem by introducing a regularization term. When building the model, this text considered the correlation between features and choose appropriate regularization parameters. The experimental results show that through the ridge regression model, this text analyzed the importance of the characteristics of the regression model, and found that the regression coefficient of Mileage Year and Tax is 5.17296619, -0.60579774 and 1.46868943 respectively, indicating that mileage, age and tax are important factors affecting the price of second-hand Audi cars [3]. This study provides a reliable method for predicting the price factors of the used Audi car market, which has an important reference value for both buyers and sellers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".