Arjuna asks, “Could you please suggest an approach to forecast multiple targets (e.g., is there a way to select multiple columns in AutoAI). In our use case, we need to develop time series forecasts for multiple products. If we correctly understood AutoAI, it will allow us to select one column at a time to generate a forecast… Is there an alternative to select multiple columns (representing multiple targets)? Thank you!”
IBM Watson Studio AutoAI doesn’t support multi-objective optimization. That’s something you’ll have to do manually with a data scientist and the built-in features for notebooks and coding like R and Python. The reason why is that multi-objective optimization is crazy costly in terms of compute. Combining it with AutoAI would blow up the servers. There’s a lot of research being done right now in this field, and this is a very cutting edge topic. Watch the video for some thoughts on workarounds.
Disclosure: My company, Trust Insights, is an IBM Registered Business Partner. Purchases of IBM software or solutions may indirectly benefit me financially.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
In today’s episode, Arjun asks, could you please suggest an approach to forecasting multiple targets? Is there a way to select multiple columns and IBM Watson Studio Auto AI and our use case, we need to develop time series forecasts for multiple products.
If we correctly understood Auto AI will allow us to select one column at a time to generate such a forecast.
Is there an alternative to select multiple columns who are presenting multiple targets? Thank you.
Okay, so there’s a lot to unpack here.
One, auto AI does not do time series forecasting Auto AI does either regression or classification.
So it doesn’t support that at all.
There are methods for doing time series forecasting.
In Watson Studio, you’d want to use the SPSS modeler for some of that.
Watson Studio Auto AI out of the box does not support multi objective optimization.
In fact, none of the auto AI Auto ML family of technologies right now support multi objective optimization.
The reason for that is that it auto AI itself is fairly costly because the the the analogy I like to use is, if you’re baking cookies, these tools are essentially varying every possible every oven temperature, every possible ingredient to see what the best overall cookie is.
That is computationally very costly.
Multi objective optimization is also very costly.
And it adds crazy, immense amounts of dimensionality.
The current technical name for is Pareto multi objective optimization.
And if you think about two people playing tug of war, right? They’re playing tug of war.
And the little ribbon in the middle of the rope is the objective right? And they’re pulling back and forth.
That’s a good example of like, single objective optimization.
You wouldn’t know somebody Got a win.
Now imagine tug of war with three people, three people holding on the ropes and there’s still you know, there’s things in the middle and each one has a thing.
And now that four or five or 10 people playing tug of war all holding different ropes, you can see how very very complex this gets.
Multi objective optimization gives you many, many different scenarios to to, to plan for.
And then Auto AI has many scenarios of each scenario.
So you can see how it just stacks up and becomes computationally unfeasible.
The way we handle multi objective optimization, most of the time, is doing what’s called a constraint based multi objective optimization where you say there’s guardrails.
So in the marketing world we have in order we’re doing SEO, we have keywords right and we have the volume of searches for keyword, we have the number of likely clicks on that.
Word, we have the cost per click, if it’s paid, we have the difficulty, we have to rank for a certain keyword.
Trying to do a four way or five way algorithm to create the best balance of all the possible outcomes is really difficult because you have to compute every possible edge case.
You know, sometimes you want difficulty 100, you’ll never rank for this keyword a lot.
That doesn’t, that’s not very sensible, right? Sometimes you want a zero dollar cost? Well, again, not necessarily all that realistic.
So what, as data scientists will do is apply constraints first into the data set before we do Auto AI on it will say, you know what, I’m not willing to pay more than seven bucks a click right.
So that immediately knocks off a certain part of the table.
I’m not interested in keywords that are, you know, above difficulty score 50 because I know my contents not that good.
So I’m not going to be able to really rank for stuff about that.
So let’s chop off that part of the table.
I’m not really keywords that have no, no search volume will drop off that part of the table.
And you can see we’re starting to apply constraints to our data set first.
So that when we stick it into something like Auto AI, we already have a much more slimmed down data set where a single objective now make sense, right? will manually look at the table.
So you know, I want to optimize for clicks.
clicks is what I care about traffic to my website.
But I’m going to apply constraints manually on those other columns.
I don’t want to below a certain volume or above a certain cost or too tough to rank for.
And then that goes into Auto AI and auto AI actually makes Auto AI much more efficient, because it has much less data to crawl through.
So you would apply those constraints in advance.
You can do this with multi objective optimization as well.
You’d apply your constraints first.
And then in Watson Studio, there’s the facility to use our or Python notebooks right within the interface and so you can write your own code to apply Using the odd the multi objective optimization library of your choice to do it there.
So, you could do that that would not get you the auto AI capability, but it will let you do multi objective optimization, you can also use the decision optimization or the see Plex facilities also within Watson Studio to do some of that if you’re not comfortable coding, again, it doesn’t get you the auto AI capability, but it does get you the decision making capability.
Finally, on the topic of time series forecasting, time series forecasting is tricky in the sense that you need to do the constraints first then you need to do the auto AI first, next, probably regression, either regression or or classification most of regression to figure out what you want to forecast what is worth forecasting.
And then you do the time she was forecasting on that.
So, that’s a three step process.
There’s you go from constraint to regression to forecast.
And that’s the process for that is not automated either.
This actually this whole question, this discussion is really good because it highlights the immense difficulty.
The data science and AI community is having with a lot of these automated AI solutions, they are good at very narrow tasks, they’re good at one thing, but the number of techniques that you can combine that your human data scientist will know to combine and in what order is very difficult to put together in a machine just have a push the button and and let the machine do its thing.
It will come in time, but it’s going to be a while.
It’s not going to be in the next quarters release.
Let’s let’s put it that way.
So to answer your question, do your constraints do Auto AI to determine which which features selectors are the most relevant to your outcome? And then due time series forecasting and again, you can do that.
In the SPSS modeler in Watson Studio, or probably you’ll use a fancier library, like any number of the Python or our libraries to really kick it up a notch after that.
The good news is within Watson Studio all that even though those are separate pieces, pieces of that can then be pushed to Watson machine learning for production use cases.
But it is, it’s this is not an easy project, but it is an interesting one, because you’re really talking about the heart of making great decisions using machine learning.
So, good question.
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