Evaluating Binary Classification Algorithms on Data Lakes Using Machine Learning
Bibliographic record
Abstract
The objective of this study was to conduct a comprehensive evaluation of binary classification algorithms within data lakes, employing a diverse array of metrics.Binary classification algorithms, which categorize inputs into one of two distinct classes, were scrutinized to determine their efficacy.The research focused on the evaluation techniques applicable to these algorithms.Methods for assessing algorithmic efficiency were investigated, including logistic regression, error function, regularization, and ancillary training tools within the dataset.A detailed analysis of the parameters pertinent to classifier evaluation was performed, encompassing accuracy, confusion matrix, precision, recall, decision threshold, F1 score, and the Receiver Operating Characteristic (ROC) curve.A critical comparison between the ROC and Precision-Recall (PR) curves was conducted, with particular attention to the Area Under the Curve (AUC) metric.The study's methodology involved training a classifier on the UCI Machine Learning Repository's Breast Cancer Wisconsin dataset, followed by the calibration of the precision/recall ratio.The findings of this study offer an in-depth examination of various evaluation metrics and threshold optimization techniques, thereby augmenting the comprehension of binary classifier performance.Practitioners are provided with guidance to select suitable metrics and thresholds tailored to specific contexts.Furthermore, the study's insights into the strengths and limitations of these metrics across heterogeneous datasets promote refined practices in machine learning and data analysis, facilitating more strategic model selection and deployment.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".