CLASSIFICATION OF TODDLER NUTRITIONAL STATUS USING A BINARY CLASSIFICATION TREE WITH ALGORITHMSQUICK, UNBIASED, EFFICENT, STATISTICAL TREE
Bibliographic record
Abstract
<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Determination of nutritional status is very important in helping to monitor the state of nutritional health growth in toddlers every time. In this study, there were 70 identity data for toddlers for the 2022 period obtained from the KB Counseling Center in the Pegajahan sub-district, in Sukasari Village. There are four independent variables used, namely gender, health insurance, weight, and height. The purpose of this study is to determine the classification that is formed and the accuracy of the resulting classification on the nutritional status of toddlers. Classification which is part of data mining can make decisions on the nutritional status of toddlers faster and more efficiently. QUEST method (Quick, Unbiased, Efficient, Statistical Trees) is one of the statistical methods that can be used to form a decision tree and classify an object using a separator algorithm that produces a binary tree. From the results of the classification there is a variable height (</span><span>𝑥</span><span>!</span><span>) </span><span>as the limiting variable. in the early stages of insulation, the parent node which consists of 70 toddler data. Variables are partitioned based on height into two nodes, namely node (1) and node (2). Node (1) is a node containing 25 children under five with a height of more than 85.79 cm, while node (2) is a node for 45 children under five with a height less than or equal to 85.79 cm. in the next process, the blocking is terminated. the overall value of the accuracy of the classification of trees formed is 95.7%. Thus, the probability of misclassification of the tree is 4.3%, which means that this classification tree is optimal.</span></p></div></div></div>
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".