How This Workflow Works
This workflow examines the relationships between numeric features in a sample dataset using several correlation methods. It compares linear and rank-based approaches to highlight different types of associations that might exist among the variables.
Key Features:
- Compare multiple correlation techniques to capture both linear and non-linear relationships.
- Identify which features are most strongly related, supporting better feature selection.
- Reveal patterns that may not be visible with a single correlation method.
Step-by-step:
1. Calculate Linear Relationships:
The workflow first measures how strongly each pair of numeric features is related using a standard linear correlation approach. This identifies straightforward, proportional relationships between variables.
2. Assess Rank-Based Associations:
Next, it applies several rank-based correlation methods. These include techniques that consider the order of values rather than their exact amounts, which can uncover associations even when relationships aren't strictly linear.
3. Evaluate Ordinal Associations:
The workflow also uses a method designed for ordinal data, measuring how well the orderings of two variables match. This can highlight connections between features that have a natural order but not a precise numeric difference.
4. Visualize and Compare Correlation Results:
Finally, the results from each correlation method are presented side by side. This lets you spot where different methods agree or diverge, making it easier to interpret the structure of your data.