Difference between revisions of "Orange: Correlations"
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Compute all pairwise attribute correlations. | Compute all pairwise attribute correlations. | ||
| − | + | ==Input== | |
| − | + | Data: input dataset | |
| − | + | ==Output== | |
| − | + | Data: input dataset | |
| − | + | Features: selected pair of features | |
| − | + | Correlations: data table with correlation scores | |
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Correlations computes Pearson or Spearman correlation scores for all pairs of features in a dataset. These methods can only detect monotonic relationship. | Correlations computes Pearson or Spearman correlation scores for all pairs of features in a dataset. These methods can only detect monotonic relationship. | ||
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| − | + | * Correlation measure: | |
| − | + | ** Pairwise Pearson correlation. | |
| − | + | ** Pairwise Spearman correlation. | |
| − | + | * Filter for finding attribute pairs. | |
| − | + | * A list of attribute pairs with correlation coefficient. Press Finished to stop computation for large datasets. | |
| − | + | * Access widget help and produce report. | |
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==Contoh== | ==Contoh== | ||
Revision as of 08:15, 28 January 2020
Sumber: https://docs.biolab.si//3/visual-programming/widgets/data/correlations.html
Compute all pairwise attribute correlations.
Input
Data: input dataset
Output
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.
- Correlation measure:
- Pairwise Pearson correlation.
- Pairwise Spearman correlation.
- Filter for finding attribute pairs.
- A list of attribute pairs with correlation coefficient. Press Finished to stop computation for large datasets.
- Access widget help and produce report.
Contoh
Correlations can be computed only for numeric (continuous) features, so we will use housing as an example data set. Load it in the File 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.
Go to the most negatively correlated pair, DIS-NOX. Now connect Scatter Plot 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 negatively correlated.
