> Do algorithmic outputs diverge or converge given variance in sequence order of all orthogonal axes? Does it matter which order the dimensions are stated in; is the output sensitive to feature order, but does it converge regardless?
Also, current LLMs suggest that statistical independence is entirely distinct from orthogonality, which we typically assume with high-dimensional problems. And, many statistical models do not work with non-independent features.
Does this model work with non-independence or nonlinearity?
Does the order of the columns in the training data CSV change the alpha of the model; does model output converge regardless of variance in the order of training data?
[LightGBM,] does not converge regardless of feature order.
From https://news.ycombinator.com/item?id=41873650 :
> Do algorithmic outputs diverge or converge given variance in sequence order of all orthogonal axes? Does it matter which order the dimensions are stated in; is the output sensitive to feature order, but does it converge regardless?
Also, current LLMs suggest that statistical independence is entirely distinct from orthogonality, which we typically assume with high-dimensional problems. And, many statistical models do not work with non-independent features.
Does this model work with non-independence or nonlinearity?
Does the order of the columns in the training data CSV change the alpha of the model; does model output converge regardless of variance in the order of training data?