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If what you want to do is keep the same proportions across the splits, what you are doing is right. In order to validate properly your model, the class distribution should be constant along with the different splits (train, validation, test). In the train test split documentation , you can find the argument: stratifyarray-like, default=None If not None, ...


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What you are proposing is a heuristic method, because you define the rules manually in advance. From a Machine Learning (ML) point of view the "training" is the part where you observe some data and decide which rules to apply, and the "testing" is when you run a program which applies these rules to obtain a predicted label. As you ...


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Generally speaking, you should investigate the process by which your values are missing and try to deal with it. I assume you checked that : There is no meaningfull way to fill those missing values. Sometimes, typically with companies data that often represent amount of money, missing values means 0$. For exemple values missing by block may correspond to a ...


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If you have a lot of data - down-sample your negative class to achieve 50/50 split on your fake news/real news classification. If you don't have much data - you can use techniques like SMOTE to up-sample the lesser class. You seem to have better accuracy than randomly choosing fake/real which is a good sign. Your probability of a negative class based on your ...


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I am afraid that such situations are fundamentally inherent in predicting/forecasting contexts; quoting from the very recent paper by Taleb et al., On single point forecasts for fat-tailed variables (open access, para 3.7): 3.7. Forecasts can result in adjustments that make forecasts less accurate It is obvious that if forecasts lead to adjustments, and ...


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I agree that the main potential issue is bias due to a particular group of instances being over-represented in the missing values. For example it's possible that the type of company is missing in cases where it's unknown or ambiguous, and this might correspond to a particular company profile (e.g. smaller, more recent, ...). To me the measures you propose to ...


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The base of any experiment or model for a machine learning problem is data. While not similar there are many models which channel faces into aging them and lowering them. This problem statement is quite similar where in one wants to know the effect a voice would have (or no effect) with the aging of a person. If you could collect audio files of peoples ...


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A few ideas: As mentioned by @weareglenn in general there is no way to know if the performance obtained on some data is good or bad, unless we know the performance of other systems which have been applied to the same task and dataset. So yes, your results are "acceptable" (at least it does the minimum job of beating the random baseline). However ...


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In an Imbalanced dataset, we don't look at the accuracy as a whole. Either check the Precision/Recall ratio Or individual classes accuracy. With that, I believe your 85% accuracy is not of much use. Individual recall are - Class_0 - 0.90 Class_1 - $\color{red}{0.40}$ It implies, 60 out of 100 fake news is missed Also, support of 95 and 471 is equivalent to ...


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