I have done a dimensionality reduction of binary labelled data (0,1 labels) from 300 features to 2 features. The plot looks like -
What kind of inferences can I make from this plot? Can I infer -
- Linear models probably would not generalize well for this data?
- Non linear models would probably be a better fit for this data?
- For this feature space (300 features), the data is non-linear?
- What inferences can be made using PCA plots?
UPDATE: More context on the question. I am solving a classification problem (a model to predict class membership). I was doing exploratory data analysis to gauge if my data is better fit for linear or non-linear models. I know there are other ways to do this QQ-plots, feature-label scatter plots etc. But can we deduce this using a PCA plot as shown above? The original 300 features have 15 discrete features & 285 continuous features.