Suppose I have a table, containing several features and a score denoting the performance (higher is better) of the corresponding features. Like this:
| feat1 | feat2 | feat3 | ... | score | --------------------------------------- | 'a' | 256 | 490 | ... | 0.336 | | 'a' | 128 | 469 | ... | 0.614 | | 'b' | 64 | 533 | ... | 0.826 | ...
Imagine this to be records of e.g. hyperparameters of an ML model and its accuracy, settings of a denoising filter and the denoising score or adjustments of a bike and the resulting comfort level. This table might contain thousands of lines and 10–100 features.
I am interested in two things:
- What features (columns) affect the score?
- What feature values (rows) lead to high scores?
So I am not asking to estimate the performance of a new setting (like a regression would do), I am asking the other way around: What settings lead to high scores? (By the way: Is there a name for this type of task? I assume this is neither classification, regression, clustering – but what is this called?)
What are (beginner friendly) methods to answer question 1 and 2? For example, I could just insert this data into a spread sheet and sort by descending score, however I might not be able to see a common pattern among the top rows.
I am sure there are some statistical methods to find patterns I am looking for. But I'm lacking vocabulary to properly search for these (I appreciate any clarifications regarding vocabulary and also references to literature).
Additional note: Some of the features are only nominal or ordinal (they are not purely cardinal)