Predicting Titanic Survivors by Using Machine Learning
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
About a century ago, one memorable night in April 1912, a world-shattering event happened. The Titanic, the 2,240-passenger luxury cruise ship, sank forever off the coast of Newfoundland in the North Atlantic after extensive damage to its hull by an iceberg on its maiden voyage. Only 705 people survived this disaster. Although nearly a century has passed, the research on Titanic has never stopped, and there are still many studies on it. This study was supposed to predict the survival of passengers on Titanic using different methods based on data from the Kaggle competition "Titanic: Machine Learning from Disaster." It predicted each passenger in the test set who would survive the sinking. The result was the percentage of correct prediction. In the Machine Learning study, the task is to achieve 80% accuracy in predicting the survival distribution of the Titanic disaster based on the demographic data testing notebook by different algorithms models. Using classification is the main point to calculate the efficiency achieved by those models through the test environment. The f-measurement scores obtained from the machine learning technology were in comparison with the f-measurement scores obtained by Kaggle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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 it