An imbalanced dataset is one in which the classes (or categories) are not represented equally. For example, in a binary classification, you might have 95% negatives and 5% positives. This poses challenges: a model can achieve high accuracy by just predicting the majority class always, yet perform poorly on the minority class which might be the one of interest (like fraud detection, where frauds are rare). Imbalance can lead to biased decision boundaries and poor generalization for the minority class because the training objective is dominated by the majority class. Solutions include resampling the dataset (undersampling majority or oversampling minority, e.g., SMOTE – Synthetic Minority Over-sampling Technique), using different evaluation metrics (like F1, AUROC which are sensitive to minority performance), or algorithms that account for class weights (giving higher loss weight to minority class errors). Proper handling of imbalanced data is crucial for tasks like anomaly detection, medical diagnosis, etc., where the rare events are important.