F1 Score = 2 * (0.5 * 0.5) / (0.5 + 0.5) = 0.5
Use of Confusion Matrix in Machine Learning
Confusion matrix is a very useful tool in machine learning. It helps in evaluating the performance of a machine learning algorithm and identifying its strengths and weaknesses. It can be used to compare the performance of different models and to select the best one for a given task.
Moreover, confusion matrix can be used to identify the type of errors made by a model. For example, if a model has a high false negative rate, it means that it is failing to identify positive instances. On the other hand, if it has a high false positive rate, it means that it is wrongly identifying negative instances as positive.
