Correlations
============

Compute all pairwise attribute correlations.

**Inputs**

- Data: input dataset

**Outputs**

- Data: input dataset
- Features: selected pair of features
- Correlations: data table with correlation scores

**Correlations** computes Pearson or Spearman correlation scores for all pairs of features in a dataset. These methods can only detect monotonic relationship.

![](images/Correlations-stamped.png){width=400px}

1. Correlation measure:
   - Pairwise [Pearson](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) correlation.
   - Pairwise [Spearman](https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient) correlation.
2. Select the attribute for computing correlations. Useful for large datasets.
2. Filter for finding attribute pairs.

Press *Finished* to stop computation for large datasets.

Example
-------

Correlations can be computed only for numeric (continuous) features, so we will use *brown-selected* as an example data set. Load it in the [File](file.md) widget and connect it to **Correlations**. Positively correlated feature pairs will be at the top of the list and negatively correlated will be at the bottom.

![](images/Correlations-links.png)

Select the most correlated feature pair. Now connect [Scatter Plot](../visualize/scatterplot.md) to **Correlations** and set two outputs, Data to Data and Features to Features. Observe how the feature pair is immediately set in the scatter plot. Looks like the two features are indeed positively correlated.

![](images/Correlations-Example.png)