With the Auto Classifier node, you can automatically create and compare a number of different models for either flag (such as whether or not a given customer is likely to default on a loan or respond to a particular offer) or nominal (set) targets.
Modeling customer response (Auto Classifier)
In this example, we'll search for a flag (yes or no) outcome. Within a relatively simple flow, the node generates and ranks a set of candidate models, chooses the ones that perform the best, and combines them into a single aggregated (Ensembled) model. This approach combines the ease of automation with the benefits of combining multiple models, which often yield more accurate predictions than can be gained from any one model.
This example is based on a fictional company that wants to achieve more profitable results by matching the appropriate offer to each customer. This approach stresses the benefits of automation. For a similar example that uses a continuous (numeric range) target, see Automated modeling for a continuous target.
This example uses the flow named Automated Modeling for a Flag Target, available in the example project . The data file is pm_customer_train1.csv.
Let's take a look at the flow.
- Open the Example Project.
- Scroll down to the Modeler flows section, click View all, and select the Automated Modeling for a Flag Target flow.