I am from CS background and moving towards Data Sciences, I have came to know ML is highly influenced by Statistical Inference/signal processing. The X we use in data science is called input, feature set or independent variables and Y is called target, class, label. What do you call it? Are they different in market and field among practitioners?
From personal experience, the majority is inputs or features for X and outputs or labels for Y. Nonetheless, other terms are also common mostly depending on the person's background (some you have already listed).
- Feature Vector
- Independent/Explanatory/Exogenous variables
- Predictor variables
- Labels (known outcomes)
- Dependent/Explained/Predicted variable
Plus, be aware of that target and labels don't always mean the same thing: for some labels represent the known outcomes for the target variable, which means that some inputs may not have a label associated to them (cf. unsupervised/semi-supervised learning).