Feature Selection
Machine Learning
A measure of how much each input feature contributes to a model’s predictions.
Detailed Explanation
Feature Importance quantifies the impact of individual input features on a machine learning model's predictions. It helps identify which features most influence the model's decisions, guiding feature selection and interpretation. Techniques like permutation importance or tree-based importance metrics are commonly used to assess and compare feature contributions, enhancing model transparency and focusing on the most relevant variables.
Use Cases
- •Use case: Prioritize key variables in model development to improve accuracy and interpretability in customer churn prediction.
Related Terms
Other terms in the Machine Learning category