Progressive Sampling-Based Joint Automatic Model Selection of Machine Learning and Feature Selection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In most machine learning applications, selecting an appropriate machine learning model requires advanced knowledge and many labor-intensive manual iterations. As a result, automatic machine learning is particularly important in order to lower the threshold for machine learning. In addition, feature selection is a very important data preprocessing process. Selecting important features can alleviate the dimension disaster problem, and removing irrelevant features can reduce the difficulty of learning tasks. The existing automatic selection methods cannot perform the automatic selection of machine learning model and feature selection model simultaneously on large-scale data. Therefore, in order to adapt to the rapid development of the era of big data, this paper proposes to establish a unified hyperparameter space for machine learning and feature selection, and adopt Bayesian optimization model based on progressive sampling for automatic model selection. By extensive experiments, we show that our approach can significantly reduce search time and classification error rates compared to the most advanced automated model selection methods.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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