Misclassification Errors

Misclassification errors refer to the mistakes made by a machine learning model when it incorrectly predicts the class or category of a data point. These errors can occur in both binary and multi-class classification problems. In binary classification, misclassification errors can be of two types: false positives and false negatives. False positives occur when the model predicts a positive class for a data point that actually belongs to the negative class. False negatives occur when the model predicts a negative class for a data point that actually belongs to the positive class. In multi-class classification, misclassification errors can occur when the model predicts a wrong class for a data point that belongs to any of the classes. Misclassification errors are a common problem in machine learning and can be caused by various factors such as insufficient data, biased data, overfitting, underfitting, and inappropriate choice of model or hyperparameters.