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From the graph It is very clear that a non-linear model will perform well to distinguish between Class A and Class B . A Linear model ( Logistic Regression) give an accuracy around 50% for such datasets . A non-linear model (For example -SVM ) with a kernel trick can give you a very good accuracy . Follow this link to see the practical difference between ...


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The usual approach with unbalanced classes is just to make the train and test sets as homogenous as possible. So make sure that proportions of the classes in both sets are the same. There are many factors that can be taken into account when splitting data, but I'm gonna guess that you just need the basic approach. In sklearn that would be any stratified ...


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What percentages are you using for buy, hold and sell classes? From the data you share in the question, I am guessing it is a stock that has been going up rather than down for the most of the days. So, if you increase percentage cutoffs you have for the stock, you will have a balanced data. As you don't share the details in your question, let's assume you ...


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The best approach is remove all the data that is not labeled 4 or 5. Then rerun all the steps. Redo the train/test split and retrain the entire pipeline from scratch, including the CountVectorizer.


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