March 2019
Intermediate to advanced
256 pages
4h 57m
French
Content preview from Machine learning : les fondamentauxBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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CHAPITRE 12 Métriques et évaluation des classifications
Dans ce chapitre, nous allons nous intéresser à plusieurs techniques de mesure et outils d’évaluation : les matrices de confusion, les principales métriques, les rapports de classification et quelques techniques de visualisation.
Ces différentes techniques seront mises en pratique pour évaluer un modèle d’arbre de décision servant à prédire la survie à bord du Titanic.
Matrices de confusion
Les matrices de confusion servent à évaluer les performances d’un classifieur.
Un classifieur binaire ne permet que quatre résultats de classification : vrais positifs TP (True Positives), vrais négatifs (True Negatives), faux positifs FP (False Positives) et faux négatifs FN (False Negatives). Seuls les deux ...
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