Imagine that you ask a yes/no question to 1,000 people, and each person answers correctly 51% of the time.
You count the different answers and pick the most common one.
How likely are you to end up with the correct answer?
🧵👇
You count the different answers and pick the most common one.
How likely are you to end up with the correct answer?
🧵👇
Don't feel bad if you think it's 51% — We all did!
If every person answers independently from the rest, you'll end up with the correct answer ~75% of the time.
And if you ask 10,000 people, the chance of getting it right goes up to ~97%!
Mind-blowing, right?
(2 / 6)
If every person answers independently from the rest, you'll end up with the correct answer ~75% of the time.
And if you ask 10,000 people, the chance of getting it right goes up to ~97%!
Mind-blowing, right?
(2 / 6)
If you care about the math behind this, take a look at the attached expression. (But it doesn't matter if you don't.)
This is what's important:
The law of large numbers ensures that we get more correct answers as we ask more people.
(3 / 6)
This is what's important:
The law of large numbers ensures that we get more correct answers as we ask more people.
(3 / 6)
Let's imagine now that, instead of people, we have many machine learning models.
If we compute our final prediction by combining all the models' answers, we will have a better chance of getting the correct result!
This illustrates the power of an ensemble model.
(4 / 6)
If we compute our final prediction by combining all the models' answers, we will have a better chance of getting the correct result!
This illustrates the power of an ensemble model.
(4 / 6)
Keep in mind that the numbers here only work if each model's prediction is independent of every other model's prediction.
This is unlikely because models will be looking at the same data.
However, combining multiple models is still much better 😎.
(5 / 6)
This is unlikely because models will be looking at the same data.
However, combining multiple models is still much better 😎.
(5 / 6)
If you are curious, we call models that do only slightly better than random guessing "weak learners."
If you combine a sufficient number of weak learners together in an ensemble, you'll get a "strong learner," which will give you much better results.
(6 / 6)
If you combine a sufficient number of weak learners together in an ensemble, you'll get a "strong learner," which will give you much better results.
(6 / 6)
If you enjoy my attempts to make machine learning a little more intuitive, stay tuned and check out @svpino for more of these threads.
• • •
Missing some Tweet in this thread? You can try to
force a refresh
