Machine Learning model generalisation

Question

I'm new to Machine Learning, and I'd like to make a question regarding the model generalization. In my case, I'm going to produce some mechanical parts, and I'm interested in the control of the input parameters to obtain certain properties on the final part.

More particularly, I'm interested in 8 parameters (say, P1, P2, ..., P8). In which to optimize the number of required pieces produced to maximize the combinations of parameters explored, I've divided the problem into 2 sets. For the first set of pieces, I'll vary the first 4 parameters (P1 ... P4), while the others will be held constant. In the second case, I'll do the opposite (variables P5 ... P8 and constants P1 ... P4).

So I'd like to know if it's possible to make a single model that has the eight parameters as inputs to predict the properties of the final part. I ask because as I'm not varying all the 8 variables at once, I thought that maybe I would have to do 1 model for each set of parameters, and the predictions of the 2 different models couldn't be related one to the other.

Thanks in advance.

Answer 1

In most cases having two different models will have a better accuracy then one big model. The reason is that in local models, the model will only look at 4 features and will be able to identify patterns among them to make prediction.

But this particular approach will most certainly fail to scale. Right now you only have two sets of data but what if it increases and you have 20 sets of data. It will not be possible for you to create and maintain 20 ML models in production.

What works best for your case will need some experimentation. Take a random sample from data and train ML models. Take one big model and two local models and evaluate their performance. Not just accuracy, but also their F1 score, AUC-PR and ROC curve too to find out what works best for you. If you do not see a major performance drop, then one big model for the entire dataset will be a better option. If you know that your data will always be divided into these two sets and you dont care about scalability, then go with two local models.