Let's assume you built two different models:
• Model 1: A ResNet model.
• Model 2: A one-shot model (Siamese network.)
They both solve the same problem, so you want to combine their results to pick the right answer.
Do you know what's better than a machine learning model?
Two models.
More than one model working together to solve a problem is called "an emsemble." A simple way to build this is having each model vote for an answer.
But there's a problem with this approach: ↓
Let's assume you built two different models:
• Model 1: A ResNet model.
• Model 2: A one-shot model (Siamese network.)
They both solve the same problem, so you want to combine their results to pick the right answer.
What happens in this case?
• Model 1's answer: Class A
• Model 2's answer: Class B
Which one do you select?
You could have 3 models, each giving you a different answer.
How do you decide which answer to choose?
Important: Some of these ideas might not be feasible depending on your context. They have worked for me before on different situations, but every problem is different.
• Take 6 months' worth of data
• Compute the prior probability of every class
• Run the data through your ensemble
• Track the results of the models
• Use performance and priors to weight these results
Let's try to break these down.
If I tell you that I saw a plane, you would believe me. But how about if I tell you I saw a UFO?
Planes have a higher chance of being the correct answer.
For example, Model 1 might be really good at identifying planes, but Model 2 may constantly make mistakes.
This should tell us how much we should believe the results from each model.
In the case of the ResNet model, the softmax probability. In the case of the one-shot model, the similarity score.
The ensemble then becomes:
• Model 1
• Model 2
• Model 3 ← This one is the new model deciding which answer to pick.
Sometimes, a simple heuristic might be a good enough solution.
It's our job to weigh the pros and cons. Better performance is just one side of the equation.
Do you have any experience dealing with ensemble voting? Any other ideas that come to mind on how to tackle this problem?
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Get a cup of coffee.
In this thread, I'll walk you through 2 probability concepts: Standard Deviation (SD) and Mean Absolute Deviation (MAD).
This will give you insight into Fat Tails -- which are super useful in investing and in many other fields.
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Recently, I watched 2 probability "mini-lectures" on YouTube by Nassim Taleb.
One ~10 min lecture covered SD and MAD. The other ~6 min lecture covered Fat Tails.
In these ~16 mins, @nntaleb shared so many useful nuggets that I had to write this thread to unpack them.
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For those curious, here are the YouTube links to the lectures:
SD and MAD (~10 min): https://t.co/0TwubymdE6
Fat Tails (~6 min):
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In essence, a Random Variable is a number that depends on a random event.
For example, when we roll a die, we get a Random Variable -- a number from the set {1, 2, 3, 4, 5, 6}.
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Get a cup of coffee.
In this thread, I'll walk you through 2 probability concepts: Standard Deviation (SD) and Mean Absolute Deviation (MAD).
This will give you insight into Fat Tails -- which are super useful in investing and in many other fields.
2/
Recently, I watched 2 probability "mini-lectures" on YouTube by Nassim Taleb.
One ~10 min lecture covered SD and MAD. The other ~6 min lecture covered Fat Tails.
In these ~16 mins, @nntaleb shared so many useful nuggets that I had to write this thread to unpack them.
3/
For those curious, here are the YouTube links to the lectures:
SD and MAD (~10 min): https://t.co/0TwubymdE6
Fat Tails (~6 min):
4/
The first thing to understand is the concept of a Random Variable.
In essence, a Random Variable is a number that depends on a random event.
For example, when we roll a die, we get a Random Variable -- a number from the set {1, 2, 3, 4, 5, 6}.
5/
Every Random Variable has a Probability Distribution.
This tells us all the possible values the Random Variable can take, and their respective probabilities.
For example, when we roll a fair die, we get a Random Variable with this Probability Distribution:
