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Theoretical flaws in machine learning decision-making mechanisms
-Due to data samples' general limitations and biases, this association learning will inevitably learn a spurious relationship. A model based on this as a decision-making basis may perform well on most test data, but in fact, the reasoning and decision-making ability based on correct causality has not been learned, and its performance will be greatly reduced when faced with new data with distribution shift from the training samples.
+Due to data samples' general limitations and biases, this association learning will inevitably learn a spurious relationship. A model based on this as a decision-making basis may perform well on most test data, but in fact, the reasoning and decision-making ability based on correct causality has not been learned, and its performance will be greatly reduced when faced with new data with distribution shift from the training samples.
Application pitfalls of machine learning