Challenge 26 - Churning Problem Part 4 - Solution

Challenge 26: Modeling Churn Predictions - Part 4

To wrap up our series of data classification challenges, consider again the following churning problem: a telecom company wants you to predict which customers are going to churn (that is, going to cancel their contracts) based on attributes of their accounts. The target class to be predicted in the test data is Churn (value 0 corresponds to customers that do not churn, and 1 corresponds to those who do). You have already found a good model for the problem and have already engineered the training data to increase the performance a bit. Now, your task is to communicate the results you found visually. Concretely, build a dashboard that:

1 - shows performance for both classes (you can focus on any metrics here, e.g., precision and recall)
2 - ranks features based on how important they were for the model
3 - explains a few single predictions, especially false positives and false negatives, with our Local Explanation View component (read more about it here: https://www.knime.com/blog/xai-local-explanation-view-component)