Huawei Certified ICT Associate – Artificial Intelligence (HCIA-AI) 2025 – 400 Free Practice Questions to Pass the Exam

Cross-validation is a widely used technique for assessing the performance of a classification model. It involves partitioning the dataset into multiple subsets, or folds, to test the model's ability to generalize to unseen data. By training the model on a portion of the data and validating it on a different subset, cross-validation helps in providing a more robust estimate of the model's performance. This technique reduces the risk of overfitting, as it allows us to evaluate how the model performs on various splits of the dataset, rather than relying solely on a single train/test split.

In contrast, normalization refers to the process of scaling input features to ensure that they contribute equally to the model's training, and while it can impact performance, it does not directly assess the model's predictive capabilities. Data visualization is essential for understanding data distributions and model behavior, but it does not provide a quantitative performance metric for a classification model. Feature extraction, which is focused on improving model inputs by selecting or transforming features, is helpful for enhancing model performance but does not serve as a performance assessment technique itself.