Classification algorithms are invaluable in machine learning, yet they face challenges with imbalanced datasets, leading to potential reliability issues in their predictions.
A confusion matrix is crucial for assessing the performance of classification algorithms, providing a visual representation of predicted versus actual labels, aiding in model evaluation.
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