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I am assuming, your focus here is the prediction accuracy and not interpretability? So, as there is a class imbalance, you can do two things: As suggested by the other user, you can use SMOTE or any technique. Use a non-parametric method that is more robust in handling the class imbalance. I tried to use Random Forest on your data, and the classification ...


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You can look into SMOTE & ADAYSN techniques. This will help you in reducing the imbalance in the dataset by creating synthetic data https://medium.com/coinmonks/smote-and-adasyn-handling-imbalanced-data-set-34f5223e167


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If your datasets are random (with no real connection between the class and predictive variables), then "the right" model is a constant one: in (A), the predicted probabilities should be roughly $0.3, 0.2, 0.5$, whereas in (B) they should be $0.33, 0.33, 0.33$. When making the hard classifier then, in (A) the maximum probability will nearly always ...


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A few comments: I don't know this dataset but it seems to be a difficult one to classify since the performance is not much better than a random baseline (the random baseline in binary classification gives 50% accuracy, since it guesses right half the time). If I'm not mistaken the majority class (class 1) has 141 instances out of 252, i.e. 56% (btw the ...


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Using lm is not the right approach to model a binary outcome. You would use a Logit in this case (see some example here and see why not lm here). However, there are (at least) two more issues: You have a highly unbalanced target You may have "noisy" features Regrading 1: You should check if some oversampling of the minority class or using SMOTE ...


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