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I am working on binary classification problem and there is 99.99% data redundancy. When I looked into the distribution of the classes both seem to be the same. Class imbalance is also part of the problem. Following is my analysis on the dataset:

  1. t-SNE Plots

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Generating t-SNE plots for different values (>4) produce same kind of graph where classes are on top of each other.

  1. PCA Analysis

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Top PCA components have very low variances spread across all of the features (total features=42).

Now I trained xgboost classifier with smote upsampling and top 5 PCA components (top 5 explains 80% of the variance). I did k fold cross validation with hyper parameter tuning. I analyzed decision boundaries for different of values of each xgboost hyper parameter. My F1 score is 0.11, which is not a good score.

Can anyone share thoughts on the current problem with all the information I provided? My main concern is if the underlying nature of the distribution might not be good from learning point of view. Because I know even if I deduplicate and train the model I might get good results but when tested on the real world data, I will have too many points from both classes which have exactly the same values (distribution).

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  • $\begingroup$ In reality classes are often not well separated and overlap substantially. You cannot expect to get what you'd call a "good result" then. In such cases the data don't allow to predict the class with much precision whatever method you use. That's just how it is. $\endgroup$ May 27, 2021 at 12:44
  • $\begingroup$ Thanks for the feedback. Yes that's the problem. Classes overlap too much to a point where classifier ends up learning too much from the seen data but never performs well on the unseen data. $\endgroup$
    – Anonymous
    May 27, 2021 at 12:54

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I dispute that the classes are so inseparable. For instance, in perplexity 4, observations around $(50, -20)$ are almost certainly going to be red, yet points around $(40, 30)$ seem to be an even mix of red and green. Since green is so outnumbered by red, a $50/50$ chance of green in a region is quite remarkable!

Now, t-SNE can create groupings that do not exist just as it can miss groupings that do exist, so that perplexity 4 plot is not necessarily indicative of how the real data look in many dimensions. Nonetheless, having clusters like this strikes me as at least a positive sign.

One of the issues perhaps leading you to have poor results is the concern with a threshold-based, improper scoring rule like $F_1$ score. Among the issues, $F_1$ score does not evaluate the XGBoost model. The $F_1$ score evaluates the XGBoost model along with a decision rule based on a threshold that might be wildly inappropriate for your task. A standard software default is a threshold of probability $0.5$. In all four of the plots, there are few regions where there will be a probability of $0.5$ of the point being green.

You might find yourself having better luck evaluating the raw outputs of your model instead of applying a software-default threshold. At the very least, you can tune the threshold to change your $F_1$ score.

Finally, it might be that the classes are simply quite similar and cannot be separated do a large extent on your data.

I will leave some links on class imbalance and the drawbacks of threshold-based performance metrics.

Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?

Profusion of threads on imbalanced data - can we merge/deem canonical any?

Why is accuracy not the best measure for assessing classification models?

Academic reference on the drawbacks of accuracy, F1 score, sensitivity and/or specificity

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