Feature importance: Correlation For measuring feature importance for numerical variables, one common approach is to use the correlation coefficient, specifically Pearson’s correlation coefficient. The Pearson correlation coefficient quantifies the degree of linear dependency between two numerical variables. The correlation coefficient (often denoted as “r”) has a range of -1 to 1: The strength of theContinue reading “ML Zoomcamp 2023 – Machine Learning for Classification– Part 7”
Tag Archives: Feature Importance
ML Zoomcamp 2023 – Machine Learning for Classification– Part 6
Feature importance: Mutual information Indeed, the risk ratio provides valuable insights into the importance of different categorical variables, particularly when examining the likelihood of churn for each value within a variable. For example, when analyzing the “contract” variable with values like “month-to-month,” “one_year,” and “two_years,” we can observe that customers with a “month-to-month” contract areContinue reading “ML Zoomcamp 2023 – Machine Learning for Classification– Part 6”
ML Zoomcamp 2023 – Machine Learning for Classification– Part 5
Feature importance: Churn rate and risk ratio Feature importance analysis is a part of exploratory data analysis (EDA) and involves identifying which features affect our target variable. Churn rate Last time, we examined the global churn rate. Now, we are focusing on the churn rate within different groups. For example, we are interested in determiningContinue reading “ML Zoomcamp 2023 – Machine Learning for Classification– Part 5”
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