Optimisation to ANN Inputs in Automated Property Valuation Model with Encog 3 and winGamma
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Bibliographic record
Abstract
An automated property model for prediction of the sales price of residential properties with optimized inputs was developed. Optimised inputs improve efficiency and speed of an Artificial Neural Network (ANN). Property appraisal ANNs have a great potential not only to save time and money but also help local government authorities to determine the tax revenue. While the criteria for the ANN’s number of hidden layer neurons are well known, there is no theory to support the optimisation to ANN inputs. The proposed optimisation to ANN inputs procedure aims to resolve some of the issues in using ANNs especially in the case of automated property valuation modelling (AVM). A brief review of ANNs and their applications is given, followed by the discussion of the ANN design methodology and optimisation. Details of ANN optimisation using Java based Encog 3 and winGamma are presented in this paper. It is shown that optimisation to ANN inputs can improve the accuracy in residential property evaluation using winGamma and Encog 3.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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