There are many instances when a single machine learning model simply isn’t good enough – you need to use multiple models. Here is what an ensemble framework looks like:
In everyday life we use a form of ensemble learning for our daily decision making. For example, suppose you are applying to a university to enrol on an ensemble machine learning course. How do you decide whether it is the right choice or not? There are always multiple things that will inform your decision:
None of these things alone are going to help you come to a final answer. But by combining each of these different components, you’ll probably come to a decision. Let’s take a look at how you can make a decision on whether you should go with this course or not. Let’s quickly analyze the scenario. As all the experts are from an independent system, we can get a very high accuracy rate as follows:
1-60%*60%*40%*40%*70%
1-0.040
96%
Can you see what we have got by the combined decision? I think it is more than you think. Can we further improve it? Yes we can; for that, we have to take suggestions from more sources, such as course faculties, a company’s workers, and so on.
The preceding example is based on an assumption that the suggestions from all the sources are independent. Well, in a practical scenario, this is not possible. If we are talking about the same domain, more or less there will be a correlation between the suggestions. Suppose we choose six sources but all are students of that course. Then we cannot reach the correct decision with high confidence; this is where the power of ensembles comes into the picture, where you have multiple predictions and you combine all of them to get a high-confidence prediction. Let’s enter the world of ensembles.
There are many reasons to go for ensembles, as each model of the group is usually based on algorithms, some of which are very simple and less computation intensive, but some may be quite complex and more computation intensive. For any production environment, accuracy and computation time are equally important. A system with higher accuracy but one that is unimplementable for real-time applications is of no use. However, a simple algorithm may lack in accuracy and may not fit onto the data properly; in those cases, we have to make a compromise between accuracy and computation time. This compromise can be minimized if we use many weak learners to get a combined confidence index out of them, which may help us to implement such a system for real-time applications with very high accuracy.
These are the main instanced when you should use ensemble learning:
As with just about everything in the machine learning world, the key thing is to select the right model for the data you have at your disposal and the questions you’re trying to answer.
If you want to learn more about ensemble learning, explore it in depth in Ensemble Machine Learning from which this post has been taken.
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