Cross-validation is a technique used to evaluate the performance of a machine learning model by dividing the data into multiple folds and training and testing the model on each fold in turn. The effectiveness of using cross-validation to enhance model performance can be interpreted in the following ways:

Study foreign trade to enhance the model

Verification is a fundamental technique in the field of learning, especially in the field of large-scale linguistic analysis. For this reason, this technique begins by exposing the model to a set of data that it has not used before during training, in order to continue to cope with new tasks and cause cases of overfitting.

How does cross-verification work?

Data partitioning: The available data is divided into several subsets, called “folds”.

Training and testing: The model is trained on a portion of the data (all folds except one), and then tested on the remaining fold to evaluate its performance.

Repeat the process: The previous step is repeated several times, using a different fold for testing each time.

Average calculation: The model's performance is averaged over all folds to estimate its performance more accurately.