A métrique par paire is a mathematical function that quantifies the distance or similarity between two items, often used in various fields such as apprentissage automatique, fouille de données, and statistics. In the context of analyse de données, pairwise metrics help to assess how closely related two data points are, which can be crucial for tasks like clustering, classification, and systèmes de recommandation.
Des exemples courants de métriques par paire incluent :
- Distance Euclidienne: This is the straight-line distance between two points in Euclidean space, calculated using the formula:
√(Σ(xi - yi)²). - Similarité cosinus: This measures the cosine of the angle between two non-zero vectors in an inner product space, providing a value between -1 and 1 that indicates how similar the two vectors are.
- Indice de Jaccard: Used for comparing the similarity and diversity of sample sets, it measures the size of the intersection divided by the size of the union of two sets.
Pairwise metrics are essential in various applications, such as content-based filtering in recommendation systems, where the goal is to find items similar to a user’s preferences. They can also be used in clustering algorithms, like K-means, where the objective is to group similar data points together based on their distances. In addition to these applications, pairwise metrics can assist in l'évaluation des performances du modèle en comparant les prédictions avec les résultats réels dans des tâches de classification.