Comparability between Pearson's Correlation and more complex machine learning models If relationships between two or more variables are found by Support Vector Machines, Random Forests, Decision Trees, and/or Extreme Learning Machines, could a simple Pearson's Correlation also detect correlations between those variables?
If yes, why? If not, why not?
I'd be thankful if you had a source I could quote for my thesis.

Using Pearson Correlation is same using Univariate Linear Regression, they both rely on the x variable and y variable satisfy linear connection. However, this is not always the case in real problems, sometimes you need multi linear regression to include many variables or you need advanced machine learning models to detect complex connections.
