Single Imputation Using Statistics-Based and K Nearest Neighbor Methods for Numerical Datasets
Bibliographic record
Abstract
Handling missing values is often an unavoidable problem.Imputation is a preferred option in handling missing values compared to removing all row records which will reduce the number of datasets and can lead to poor research results if the size of the remaining data is too small.The problem that often occurs is that there are often wrong conclusions due to some records that have missing values, therefore this study will test several simple imputation methods, namely statistical-based imputation and kNNI.The results of testing the error value with RMSE and MAPE show that kNNI imputation results are much better than statistical-based imputation.Based on the standard used in the MAPE test, the kNNI test results (error values) are almost entirely very good because the error value is <10% except for three test results in dataset 1 at k=10, k=15 and k=20, while the statistical-based imputation results are only good because the error value is between 10% and 20%, even one of the results exceeds 20% Although kNNI is better than statistical-based imputation, it is necessary to choose the right k value to get the best imputation results.
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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 | 0.024 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".