Thank you for putting up with me during my foray into traditional machine learning.
I think it was useful for us and I hope that you didn't mind it too much.
Maybe you enjoyed yourself a little bit like I did and tried out your own models too.
Let's just look at how they appear side by side.
We started with a random model, which came in at a somewhat shocking $341 off from the from the reality.
And then we tried a constant model that did a whole lot better, but was still $146 wrong.
We then did some proper models, the features and and linear regression model.
At 139 we did a whole lot better with a bag of words model, the Countvectorizer and $114.
We were slightly disappointed that when we layered on the powerful word two vec, it came in at $115.
You may have noticed that there were 400 dimensions of word two vec, whilst there were a thousand dimensions
in the bag of words model, But still, you would expect that the 400 dimensions in the word two vec
would be just so much.
There would be so much more signal in those vectors that you would expect better results.
So whilst that was a bit disappointing, we quickly made up for it.
First of all, by getting a hair better when we use support vector machines, but then random forests
save the day with a nice $97 error there.
And you know, there's there's this potential school of thought that would be to say that $97 is still
disappointing given just predicting the the price of a product.
But I'll tell you something.
I challenge you yourself to go in and pick some of those products and blindly try and price them.
It ain't easy.
It's surprisingly difficult.
You saw when we were confronted with that LED light, that we looked at that example a moment ago,
I think, and I don't know if I had seen that, I would have probably have guessed that's about $40
or something.
And it was 200 and something.
So, you know, it's actually surprisingly hard just given a description of something to figure out.
Where is this on a scale?
And so getting within $97 based purely on a description of some product that could be electronics,
it could be an appliance, it could be any of those other automotive, of course, any of the other
things, the categories that we picked, it's it's not as easy as it sounds.
And so getting within $97 of it on average across our test set is not bad at all.
Not bad at all.
But potentially we'll be able to do better.
We will see.
All right.
So well done on getting to this point.
It's been a lot of fun for me.
Anyway.
You've tolerated me and hopefully you didn't mind it.
But fear not, the time has arrived for us to go to the frontier.
So, uh, next time we're going to be talking about solving commercial problems using frontier models,
we are then going to run that runner against GPT four mini and see how it fares.
And then I'm going to be brave.
I'm going to set our sights high, and we are going to run our test dataset against the big guy, against
GPT four zero maxi, the full version, the frontier version from August.
And that's going to be a big test for us.
We'll see how it does.
And yeah, remember, it's quite a challenge for an LLM because we're we're basically we're not going
to give it any training data.
Unlike these traditional models where we've given them training data, we're simply going to send the
test data to the LLM and say, given all of your worldly knowledge, how much do you think this is going
to get and how much do you think it's going to be worth?
And that's not an easy problem to set.
So in many ways, the traditional machine learning models have a big advantage that they've been trained
based on a training data set.
In the case of these frontier models, we're just going to give them the descriptions and say, okay,
how much is this?
We will see how they get on in the next video.