A Machine Learning Approach on Outlier Removal for Decision Tree Regression Method
Bibliographic record
Abstract
Outliers can occur in application areas, adversely affecting the prediction method's performance.Outliers can be removed by using robust statistical algorithms.However, statistical methods have limitations in capturing the outlier for high-dimensional data.Approaches using Machine Learning (ML) are offered as they develop rapidly due to their excellent interpretability and strong generalization capabilities.So, ML is popular in detecting or eliminating outliers to increase the accuracy of forecasting methods, such as Isolation Forest (IF), an unsupervised outlier detection strategy using a collective approach to calculate the isolation score for every data point.This research objective is to improve the prediction accuracy of the Decision Tree Regression (DTR) method by proposing an IF as an ML-based outlier removal method.The proposed method was tested by two Air Quality Index (AQI) dataset that contained outliers with Mean Absolute Error (MAE), R-Square, and Root Mean Square Error (RMSE) as the accuracy measurements.The results showed that the proposed method outperforms previous studies.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".