Precision , reconition rate, and accuracy ?

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Sergei V. Dolin

Hello, Shima Shafiee.

1) Precision can be interpreted as the proportion of objects that are called positive by the classifier and are actually positive

[precision = TP / (TP +FP)]

2) About Recognition Rate (https://www.sciencedirect.com/science/article/abs/pii/B9780120884520500042)

3) Accuracy - the proportion of erroneous answers of the algorithm [accuracy = (TP + TN) / (TP + TN + FP + FN)]

If your question meant something else, please write

Safiul Haque Chowdhury

Precision, recognition rate, and accuracy are metrics used to assess the performance of classification models, such as those used in machine learning and pattern recognition. Let's define each term and discuss their differences:

Precision: Precision is a measure of the accuracy of the positive predictions made by a classifier. It is the ratio of true positives (correctly predicted positive instances) to the sum of true positives and false positives (instances predicted as positive but are actually negative). Precision is concerned with the precision of the positive predictions and is expressed as:Precision=True PositivesTrue Positives+False PositivesPrecision=True Positives+False PositivesTrue Positives A high precision indicates that when the classifier predicts a positive instance, it is likely to be correct. Precision is especially relevant in scenarios where the cost of false positives is high.

Recognition Rate: Recognition rate, often referred to as accuracy, is a measure of overall correctness in a classification model. It is the ratio of the total number of correct predictions (true positives and true negatives) to the total number of instances. Accuracy is expressed as:Accuracy=True Positives+True NegativesTotal InstancesAccuracy=Total InstancesTrue Positives+True Negatives While accuracy is a commonly used metric, it may not be suitable in situations where the classes are imbalanced. In imbalanced datasets, a high accuracy might result from correctly predicting the majority class but might overlook the performance on the minority class.

Difference Between Precision and Recognition Rate:The key difference lies in what each metric is emphasizing. Precision focuses on the accuracy of positive predictions, providing insights into how well the model performs when it predicts a positive outcome. On the other hand, recognition rate or accuracy provides an overall measure of correctness, taking into account both true positives and true negatives.

It's important to note that the choice of evaluation metric depends on the specific goals and characteristics of your classification problem. Precision is valuable when false positives are costly, while accuracy is a more general measure of overall correctness. Additionally, other metrics like recall and F1 score are often considered in conjunction with precision to provide a more comprehensive evaluation of a classifier's performance.

Shima Shafiee

Thanks, dear Safiul Haque Chowdhury, your guidance is complete