Classificateurs bayésiens naïfs | Naive Bayes
Bibliographic record
Abstract
Nous verrons dans ce tutoriel comment détecter des modèles à l’aide du classificateur bayésien naïf, une technique d’apprentissage-machine efficace pour détecter certains modèles et prévoir les dépendances au sein de votre jeu de données. Nous réexaminerons dans la première partie de ce tutoriel le jeu de données Iris utilisé dans le tutoriel précédent pour apprendre à utiliser le classificateur bayésien naïf. Nous appliquerons par la suite vos nouvelles connaissances pour déceler les pourriels parmi vos messages textes (SMS), de manière à identifier les messages que vous ne désirerez pas lire. Le jeu de données que nous utiliserons s’agit d’un jeu de données de source libre du Référentiel d’apprentissage-machine UCI. Nous examinerons ensuite la classification multi-étiquettes via le jeu de données CMU que nous avons utilisé antérieurement pour le classificateur des plus proches voisins. Enfin, nous vous donnerons un exemple d’utilisation non aboutie du classificateur bayésien et vous expliquerons pourquoi cela n’a pas fonctionné. The tutorial revisits the Iris flower dataset to introduce the basic steps of working with the Naive Bayes Classifier. It then applies the classifier to detect spam in SMS messages using the SMS Spam collection dataset from the UCI Machine Learning Repository, and performs multi-label classification using the CMU book dataset. The tutorial also presents a scenario where the Naive Bayes Classifier fails, providing an explanation for the failure. By the end of this tutorial, participants will have a solid understanding of the Naive Bayes classifier, be able to split data into training and testing sets, make predictions, evaluate classifier performance, identify spam, classify books, train a Gaussian Naive Bayes classifier for single or multiple labels, and utilize imputation techniques for handling missing data.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".