Which technique is used to assess the performance of a classification model?

Cross-validation is a technique specifically designed to evaluate the performance of a classification model (as well as regression models) by partitioning the data into subsets. In this process, the model is trained on a specific portion of the data while being tested on a different subset. This method allows for a more reliable assessment of how well the model generalizes to unseen data, reducing the likelihood of overfitting.

By evaluating the model's performance across various subsets of data, cross-validation provides a more robust estimate of the model's accuracy and can highlight potential issues that might not be visible when using a single train-test split. This helps in selecting the optimal model parameters and assessing the model's stability across different data samples.

Data normalization plays a critical role in data preprocessing by ensuring the input features have a consistent scale, but it is not a direct method for assessing model performance. Feature selection aims to identify the most relevant features to improve model efficiency and accuracy, but similar to normalization, it does not assess performance. Clustering is an unsupervised learning technique used to group similar data points, which is different from classification tasks that require labeled data for performance evaluation.