1. What it looks like
…as generated by EvalC3
2. How read this Decision Tree
The tree should be read from left to right, as though it is a tree that has fallen over
This tree has 7 branches, each with a “leaf” at the end, shown by the color beige or green. Each of these branches is a prediction model , made up of a particular configuration of case attributes, described by the text labels.
The green and beige leaves describe the numbers and types of outcome found.
Beige= outcome absent. Green = outcome present. This is the predicted outcome of this model, given the distribution of cases on the leaf.
First number = Number of cases with outcome present. Second number = Number of cases with outcome absent. Essentially the same as the top row of the Confusion Matrix.
The example here uses data from the Krook QCA study of women’s participation in parliament in 26 African countries
Let’s read the top branch…Where “quotas” are absent (0) and “Women’s status” is absent (0) this model find there are 0 countries with high levels of women’s participation in parliament, but there are 12 cases where there are low levels of women’s participation in parliament.
In the next branch…Where “quotas” are absent (0) and “Women’s status” is present (1) this model find there are no countries with high levels of women’s participation in parliament, and a “post-conflict situation” is absent (0) there are 0 countries with high levels of women’s participation in parliament, but there is 1 case where there are low levels of women’s participation in parliament.
In this example, each branch represents a configuration that is sufficient for the outcome being either present (green) or absent (beige). But sometimes both outcomes will be present, but one will be more common than the other. In other words, the model will have some inconsistency (in QCA terms) and have limited “Positive Predictive Value” or “Precision” – to use terms used elsewhere.
3. What to do with it
To view a particular model in detail, click on the 0 or 1 cell to the left of the leaf you are interested in.
Then click on Load Model. This will take you to the Design & Evaluate view, where you will see the model attributes in the Design section and its performance measures in the Evaluation section
To save this model, click on Save Model
5. Where to learn more about Decision Tree and how they work
- “A visual introduction to machine learning” – I rate this as Excellent!