Скачать или смотреть Correlations and Multi-collinearity in Feature Engineering

Correlations and multicollinearity are crucial concepts in feature engineering, both addressing the relationships between features in a dataset. Here's a concise overview:

Correlations:

Correlation measures the statistical association between two variables. In feature engineering, understanding correlations helps identify how features move together, guiding decisions on feature selection and model interpretability.
Multicollinearity:

Multicollinearity occurs when two or more features in a dataset are highly correlated, potentially leading to redundancy. Detecting and addressing multicollinearity is vital in feature engineering to enhance model stability and prevent inflated coefficient estimates.

code :
https://github.com/gridflowai/Youtube...