Study on the Relationship Between City and District Average Price by GAOT in Taipei
Bibliographic record
Abstract
Since 2012, Taiwan enforces real price login, people can reference data and understand the changing price of various regions. The housing price of this study, therefore, is designated by unit price of trading contract. Taipei City, one of five municipalities in Taiwan, is found the unit price of trading contract is the most than others in an upward trend in its database from the third quarter of 2007 to the second quarter of 2012 (total twenty seasons). So, this study explores the relationship with others between unit price of trading contract overall in Taipei and unit price of trading contract of 12 districts. It is going to use autonomic architecture model with the ability of genetic algorithm operate tree (GAOT) to find out the best combination of variables and establish the relationship mode between each other. It also can assess overall real estate trends in Taipei City by important changing of districts. Research statistical analysis shows correlation coefficient (CC) for unit price of trading contract is 0.98 in Daan district and overall Taipei City. The result shows a high degree of correlation. After GAOT operator, besides, the establish assessment model could be found. It only uses unit price of trading contract of four districts. The unit price of trading contract of overall Taipei can be known. The coefficient of determination (R2) is up to 0.996 and root mean squared error (RMSE) is 0.406. The results are better than multiple linear regression (MLR) (R2=0.994, RMSE=0.490) and appear that GAOT can predict accurately of overall Taipei. It exist the relationship with each other between four districts and Taipei. In other words, the trend of variable is in coincide circumstance. The advantage of unit price of trading contract can be later used, therefore, this kind of mode to statistic four districts. The forecasting process for unit price of trading contract overall Taipei engages the government and civil society to assess overall real estate market of urban area.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".