Orange Workflows

Where Are Misclassifications

Cross-validation of, say, logistic regression can expose the data instances which were misclassified. There are six such instances for iris dataset and ridge-regularized logistic regression. We can select different types of misclassification in Confusion Matrix and highlight them in the Scatter Plot. No surprise: the misclassified instances are close to the class-bordering regions in the scatter plot projection.


Cross Validation

How good are supervised data mining methods on your classification dataset? Here’s a workflow that scores various classification techniques on a dataset from medicine. The central widget here is the one for testing and scoring, which is given the data and a set of learners, does cross-validation and scores predictive accuracy, and outputs the scores for further examination.


Classification Tree

This workflow combines the interface and visualization of classification trees with scatter plot. When both the tree viewer and the scatter plot are open, selection of any node of the tree sends the related data instances to scatter plot. In the workflow, the selected data is treated as a subset of the entire dataset and is highlighted in the scatter plot. With simple combination of widgets we have constructed an interactive classification tree browser.