Feature Importance
Feature Importance is a technique used in Data Science and Machine Learning to determine the most important features or variables that contribute the most to the performance of a model. It helps in identifying the key factors that affect the outcome of a model and can be used to improve the accuracy of the model by selecting only the most relevant features. Feature Importance can be calculated using various methods such as permutation importance, mean decrease impurity, and SHAP values. These methods assign a score to each feature based on its contribution to the model's performance. Feature Importance is an important step in the model building process as it helps in reducing the dimensionality of the data and improving the interpretability of the model.
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