So I am new to machine learning and I want to take on a small project to get my hands dirty.

I want to train a model to detect a very specific pattern on a given data. Given a set of numbers, I want it to be able to tell if the numbers follow a very specific pattern (so kind of classification model - 1 if the data follows the pattern and 0 if not).

Let's say its a really arbitrary set of rules, a series of 10 numbers follow the pattern if all the following conditions are true:

• There is at least one possible way to divide them into 2 groups of 5 numbers where the sum of one group is equal to the sum of the other group.

• The first 3 numbers are arranged in ascending order

• The last 3 numbers are arranged in descending order

I can obviously generate a lot of samples which follow these rules, and tell the model that they are classified as 'True'. But what do I do to teach it the 'False' classification? There isn't really a way to classify a 'False', there is only a way to classify a 'True'. The only way to classify a 'False' is if it's not classified as 'True'.

I can generate samples which doesn't follow these rules, but won't it just confuse the training algorithm? Won't it try to "figure out" a pattern for those samples as well? While there isn't such.

I want a model which "understands" that it only has to check if a sample belongs to one group and if it's not, it belongs in the other group, which might not have a common pattern.

What is the best way to train such model? Is there a different kind of model which is more suitable for this situation?