diff --git a/README.md b/README.md
index 8bf3fa0..e11a4b2 100644
--- a/README.md
+++ b/README.md
@@ -46,7 +46,7 @@ change(df)                             # every numeric column at once
 
 ### `vif`: Variance Inflation Factor (Multicollinearity)
 The classic multicollinearity check, without the six-line loop.
-> _Underlying library:_ `statsmodels.stats.outliers_influence.variance_inflation_factor`
+> _Similar Concept:_ `statsmodels.stats.outliers_influence.variance_inflation_factor`
 ```python
 from percentify import vif
 
@@ -84,7 +84,7 @@ cv(df)             # → DataFrame of every numeric column, most variable first
 
 ### `outliers`: Percentage of Outliers (IQR Method)
 Stop rewriting the IQR bounds from scratch.
-> _Underlying library:_ `scipy.stats.iqr`
+> _Similar Concept:_ `scipy.stats.iqr`
 ```python
 from percentify import outliers
 
@@ -98,13 +98,13 @@ from percentify import r_squared
 
 r_squared(y_true, y_pred)   # → 87.3
 ```
-> _Underlying library:_ `sklearn.metrics.r2_score`
+> _Similar Concepts:_ `sklearn.metrics.r2_score`
 
 ### `pca_variance`: PCA Variance Breakdown
 Columns are standardized by default, so a feature measured in large units (e.g.
 dollars) can't dominate the result just because of its scale. Pass
 `standardize=False` for covariance-based PCA on the raw values.
-> _Underlying library:_ `sklearn.decomposition.PCA` (`.explained_variance_ratio_`)
+> _Similar Concept:_ `sklearn.decomposition.PCA` (`.explained_variance_ratio_`)
 ```python
 from percentify import pca_variance