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In a predictive model that uses many features, some important variables are missing and errors are large in a specific patient group. What is the most likely cause?
①Missing values are not being handled properly, causing bias.
②Multicollinearity among features is making the model unstable.
③The model isn't converging because the learning rate is too high.
④The labels contain a lot of random noise.
⑤The training data is overly normalized.