CV Accuracy
时间: 2023-11-04 09:06:28 浏览: 38
CV Accuracy refers to the accuracy of a machine learning model that has been trained and tested using cross-validation techniques. Cross-validation is a technique used to evaluate the performance of a model by splitting the data into multiple folds and training the model on different combinations of these folds.
The CV accuracy of a model is calculated by averaging the accuracy scores obtained from each fold of the cross-validation. It is an important metric that indicates how well the model is able to generalize to new, unseen data.
Higher CV accuracy indicates that the model is more accurate and has a better ability to predict outcomes for new data. However, it is important to note that CV accuracy alone may not be sufficient to evaluate the performance of a model, and other metrics such as precision, recall, and F1 score should also be considered.