Case Study: Measure Feature Importance for Healthcare

Learn how to measure feature importance while building complex machine learning models.

In this lesson, we’ll learn how to compute feature importance for the hospital readmission case study.

Feature importance is a key concept in machine learning and data analysis because it helps us understand the contribution of each feature (or variable) in a dataset to the predictions or outcomes made by a model.

Feature importance techniques assign scores to input features based on how valuable they are in making accurate predictions. The scores can come from various sources and methods. Some common examples include statistical correlation scores, coefficients from linear models, decision trees, and permutation importance scores.

By understanding feature importance, we can gain insights into which features have the most influence on predictions. This information is vital for decision-making and understanding the factors that drive the model’s performance.

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