Model Accuracy

Model accuracy, also known as model precision, is a measure of the ability of a machine learning (ML) model to make predictions or decisions based on data. It is a common metric for evaluating the performance of ML models and can be used to compare the performance of different models or to evaluate the effectiveness of a specific model for a given task.

How to measure the accuracy of a computer vision model?

There are a number of different ways to measure model accuracy, depending on the type of machine learning model and the nature of the problem being solved. Some common methods include classification accuracy, mean squared error, and mean absolute error.

Classification accuracy is a common measure of model accuracy for classification tasks and is defined as the proportion of correct predictions made by the model. It is usually calculated by dividing the number of correct predictions by the total number of predictions made by the model.

The mean squared error (MSE) and mean absolute error (MAE) are commonly used to measure the accuracy of regression models, which are used to predict continuous values. MSE is defined as the average of the squared differences between the predicted values and the true values, while MAE is defined as the average of the absolute differences between the predicted values and the true values.

In addition to these metrics, it is common to use other model accuracy measures such as precision, recall, and F1 score, which are particularly useful for imbalanced classification tasks.

Overall, model accuracy is an important metric for evaluating the performance of machine learning models and is used to assess the effectiveness of different models and compare their performance.

References

【1】https://encord.com/glossary/model-accuracy-definition/