Train a classification model using the Decision Tree algorithm. Evaluate the accuracy of the class prediction by scoring metrics, ROC Curve, and Lift Chart.
This is a companion discussion topic for the original entry at https://kni.me/w/wWrebA_HNv4hHDDG
Great job @Maarit !
Finally we have a simple brand new workflow showing how to score classification models!
Thanks @paolotamag! Yes - those interactive views are nice on their own like this, but even more powerful when combined with other views in components. I am currently working on more of these examples, and they will all be shared here via the Workflow Hub!
Related to this topic of evaluating model performance.
- testing classification models (different types or just different features etc) to see which perform the best on the data you have
- would like to be able to connect multiple Predictors into the Scorer… and the Scorer retains the input table name or Node name… that way the Lift and ROC curve can be plotted on one graph so you can see performance across models
You can currently do this with the new Binary Classification Inspector node - check out some of the example workflows featuring it on the Hub!