Correlation between two features refers to the relationship between two variables in a dataset. By plotting one variable agains another on a graph, which will be colored with respect to the target variable, you can distinguish the relationship between two features more distinctively. For instance, we can observe there is a strong positive correlation between Spent and Clicks features for a particular ROAS range on the variable correlation graph below. We can see that for a ROAS in range -1.8 to 11.32 (green colored points) there is a linear relation between Spent and Cliks. Also for a ROAS range from 11.32 to 24.43 (blue colored points) we see this linear relationship. It would be extremely difficult to notice such a distinct observation through a simple pearson matrix.
By understanding the correlation between two features, we can use this information in several ways, including:
Understanding model performance: Correlation can help to explain why a model is performing well or poorly. If a feature that is highly correlated with the target variable is not included in the model, this may lead to poor performance.
Outlier detection: Correlation can help identify outliers, which are observations that are far from the other data points. For example, if two features are highly correlated, an observation with a large deviation in one feature may indicate an outlier in the other.
Data preprocessing: Correlation can be used to identify and remove redundant features, normalize the data, or handle multicollinearity, which is the situation where two or more features are highly correlated.
Overall, variable correlation is an important aspect of exploratory data analysis and can provide valuable insights into the relationships between features in a dataset, which can inform the development of machine learning models.
Updated on: 02/02/2023