If you have a perfectly balanced dataset (unlike Imbalanced Datasets) but still experience poor classification accuracy, class separability might be an issue due to the following reasons:
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Overlapping Classes: The features of different classes may overlap significantly, making it difficult for the model to distinguish between them. If the decision boundaries are not well-defined, the model may struggle to classify instances correctly.
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Complex Decision Boundaries: The underlying relationship between the features and the classes may be complex, requiring a more sophisticated model to capture the nuances. If the model is too simple, it may not be able to learn the necessary patterns.
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Noise in the Data: If there is a significant amount of noise or irrelevant features in the dataset, it can obscure the true signal, leading to poor classification performance despite balanced class representation.
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Insufficient Feature Representation: The features used for classification may not adequately represent the underlying characteristics that differentiate the classes. This can lead to a lack of separability, even in a balanced dataset.
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Model Overfitting or Underfitting: If the model is overfitting, it may perform well on training data but poorly on unseen data. Conversely, if it is underfitting, it may not capture the complexity of the data, leading to poor accuracy.
Addressing these issues may require exploring different feature engineering techniques, selecting more appropriate models, or adjusting hyperparameters to improve class separability.