Ensemble learning allows you to create more accurate models by combining the power of multiple models: DataRobot calls these types of models “blenders.”
Figure 1: Ensemble Learning
Blending and Stacking
DataRobot uses two major techniques to create blenders. The first is to simply take the means or medians over several models for each observation and use that as a prediction. The second is to take the predictions from several models and use them as features in a final model; this is called stacking (Figure 2).
Figure 2: Ensemble example
Why is this done?
Creating blenders often results in more accurate models.
Blenders allow you to use multiple blueprints.
And blenders allow you to leverage the wisdom of crowds principles.
What are some considerations?
It is generally advised to blend models that rely on different algorithms and have good accuracy.
While blenders can give you a boost in accuracy, they can often take more time to create and score because they are more complex.
Blender models can make the final model more complicated to communicate to regulators; however, DataRobot model interpretability tools and documentation can help you with this process.
By default, DataRobot will create four blenders for each Autopilot run (Figure 3). This includes two blenders that blend the top 3 models and two blenders that blend the top 8 models.
Figure 3. Leaderboard
If you're curious about any of these models, all you have to do is click on the blender (model) and the blender blueprint will be displayed (Figure 4).
You can see within this blueprint the preprocessing steps for the blender. Notice that these steps include other models that you can find on the Leaderboard.
The final step is the actual blending. If you want to look at any of these steps in more detail you can simply click one of the blueprint boxes and, in the displayed pop-up, select to view DataRobot Model Docs.
Figure 4. Blueprint
The same metrics that you use to evaluate and interpret your other models apply here as well (see Figure 5).
Figure 5. Evaluation
You can also go to the Understand division to see Feature Impact,Feature Effects, and Prediction Explanations (Figure 6).
Figure 6. Interpretation
Create your own blender
If you want to create your own blender, all you have to do is select models that you want to blend. These two strategies can help you get the most out of your blenders.
Stay near the top of the leaderboard, so you will use the most accurate models.
Use models that rely on different algorithms.
You can then simply go to the menu and, under the section called blending, you can select a number of different types of blenders (Figure 7).
Figure 7. Types of blenders
This is going to start the blending process and you can see this processing on the right-hand side of the screen. When the blender is complete, it will be added to the Leaderboard and ranked among the other models that you've created.