https://safirsoft.com Our AI title test continues: Did we break the device?

In Part Three of Four, we look less at what went right and what went wrong.

We are now in the third phase of machine learning projects - that is, we have gained the denial and anger of the past, and we are now entering into bargaining and depression. I'm required to use Ars Technica's dataset from a five-year title test, which brings two ideas together in an "A/B" test to allow readers to use whichever article. The goal is to try to build a machine learning algorithm that can predict the success of any given address. And since my last arrival, he hasn't been on time...

I also spent a few bucks in an Amazon Web Services time account to find out. The test can be a little expensive. (Tip: Don't use "auto-pilot" mode if you have the budget.)

We've tried several ways to split 11,000 titles out of the first 5,500 half-winning tests. half loser. First, we took the whole collection in comma-separated values ​​and tested "Hello Mary" (or, as I've seen it in the past, "Leeroy Jenkins") using the autopilot tool in SageMaker AWS studio. This came with a 53% correctness score. This is unlikely to be a bad thing, because when I used a natural language processing model - AWS's BlazingText - the result was 49% accurate, or even worse than a coin toss. (By the way, if most of this seems pointless, I'd recommend checking out Part 2, where I use these tools in more detail.)

Simon, an evangelist at AWS, was lacking in our data. Using an alternative model for our data set in the binary classification mode, only 53–54% accuracy is obtained. Now it's time to see what's going on and if we can fix it with some changes to the learning model. Otherwise, it may be time to take a completely different approach.

Our AI title test continues: Did we break the device?
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