👉 Misclassification is a phenomenon in which an algorithm or model incorrectly assigns a class to a sample that belongs to another class. This can happen due to various factors, such as noise, overfitting, or insufficient data. Misclassification can occur in both supervised and unsupervised learning settings. In supervised learning, it occurs when the model is trained on a set of labeled data where the correct label for each example is known. When misclassified examples are seen by an algorithm that does not recognize them