Accuracy is a fundamental metric in the taxonomy that provides a direct measure of how well the model performs the expected task.
Accuracy represents the ratio of the correct number of instances to the total number of instances in the dataset. In short, it answers the question: "Out of all the **s, how many are true?".”
（tp）：The model is correct and positiveInstance.
(tn): The model accurately captures the negative classes.
(fp): These are the cases of model error positive classes.
(fn): This refers to the fact that the model fails to recognize a positive class and instead marks it as a negative class.
Accuracy is often the first metric to consider when evaluating a classification model. It is easy to understand and provides a quick snapshot of the model's performance. For example, if the model is 90% accurate, it makes the correct ** for 90 out of every 100 instances. However, while accuracy is valuable, it's also important to know when to use it. In cases where the categories are relatively balanced and the cost of misclassification is the same for each category, accuracy can be a reliable indicator.
AI Assistant Creation Season In addition, in real-world scenarios, the cost of different types of errors may vary. For example, in a medical diagnosis, a false negative (failure to identify a disease) may have more serious consequences than a false positive.