Ranking Metrics
In ranking tasks, where the goal is to predict the order or ranking of items, evaluation metrics are tailored to assess the quality of the ranked lists.
- Normalized Discounted Cumulative Gain (NDCG)
NDCG is a widely used metric for ranking tasks. It measures the quality of a ranked list by considering the relevance of items and their positions in the list. It’s especially common in recommendation systems.
- Precision at K
Precision at K measures the proportion of relevant items in the top K positions of the ranked list. It’s used to evaluate how well a model ranks relevant items at the top.
- Mean Reciprocal Rank (MRR)
MRR calculates the average reciprocal rank of the first relevant item in the ranked list. It provides a single value that summarizes the model’s ability to rank relevant items highly.
LightGBM Model evaluation metrics
LightGBM (Light Gradient Boosting Machine) is a popular gradient boosting framework developed by Microsoft known for its speed and efficiency in training large datasets. It’s widely used for various machine-learning tasks, including classification, regression, and ranking. While training a LightGBM model is relatively straightforward, evaluating its performance is just as crucial to ensuring its effectiveness in real-world applications.
In this article, we will explore the key evaluation metrics used to assess the performance of LightGBM models.