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Correlation Matrix
Display pairwise correlations between all variables in a dataset as a colored grid.
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Correlation Matrix · Example Data
When to use a Correlation Matrix
- Exploratory data analysis with many numeric variables
- Feature selection for machine learning models
- Understanding multicollinearity in regression
- Scientific reporting of variable relationships
About the Correlation Matrix
A correlation matrix is a special heatmap that shows the Pearson (or Spearman) correlation coefficient between every pair of variables in a dataset. Values range from -1 (perfect negative) to +1 (perfect positive), with 0 meaning no linear relationship.
It is a foundational tool in exploratory data analysis (EDA) and feature selection for machine learning. The matrix is symmetric, so only one triangle needs to be shown.