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Thank you!
I use stratified k-folds cross-validation to deal with the imbalanced data (keep the proportion on the data in training and test) and to make sure the model is good to generalize.
Since the dataset is imbalanced, a better measure for performance is f1-score, so I use it to compare and select those methods. The process can be improved in many ways, like making plots and make the selection of others hyperparameters (like kernels, gamma, solvers, etc.) in an automated way. Also, sci-kit learn must have functions to make the best model selections.
Between those 4 models, they can be compared using plots to see generalization capabilities, but they seem pretty similar. I would use Logistic Regression because it gives you probabilities, not only labels.
Some references:
https://medium.com/@mohtedibf/indepth-parameter-tuning-for-decision-tree-6753118a03c3
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html
https://machinelearningmastery.com/k-fold-cross-validation/
https://medium.com/analytics-vidhya/accuracy-vs-f1-score-6258237beca2
from coursera-ibm-project.
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