diamondketo

Not pretentious with the AI term. Most of us DS/ML folks just accept it and it pays our wages because AI is a cooler term than ML which is cooler than statistical inference/learning.

A tree in Primeclue can be thought of as a complicated equation

In most cases, a neural network models a non-linear equation. However, a neural network can also be built to model a linear equation (e.g., perceptron algorithm).

So I find your claim extremely unbelievable that you cannot solve a simple linear relation by having Primeclue model ("be thought of as") a linear equation to do simple linear regression.

One thing Pandas does right is fluent syntax (df.rename.set_index.groupby.etc). I rarely seen it on other popular packages (maybe sqlalchemy) uses it.

Are you not interested in validating whether your algorithm is (1) new and (2) works better than neural network and classification decision trees?

How are the function nodes in the trees built? Does the user specifies them based on their model or does your algorithm learn to choose the best functions?

Are the non-data leaf nodes free parameters?

• If yes, how does your algorithm optimize and estimate the best parameter? I understand your algorithm prunes the tree; how does free parameter and pruning come together; do you optimize the free parameter first then prune?
• If no, how does your algorithm choose the best parameter (e.g., why e, why pi?)