Which is a common method for assessing the validity of a machine learning model's performance?

Data splitting is a common method for assessing the validity of a machine learning model's performance because it allows for the creation of separate datasets for training and evaluation. In this process, the available dataset is divided into at least two parts: a training set and a test set. The model is trained on the training set, which helps it learn patterns and relationships within the data, and then evaluated on the test set, which is used to determine how well the model generalizes to new, unseen data.

This method reduces the risk of overfitting, where a model performs exceptionally well on training data but fails to predict accurately on new data. By validating performance on a separate test dataset, one can gain insights into the model’s robustness and predictive capabilities. The practice of data splitting also sets a standard in machine learning workflows for fair performance assessment and is critical for understanding the model's reliability in real-world applications.

In contrast, text mining, feature extraction, and web scraping are techniques applicable in different contexts for handling and analyzing data or gathering information. They involve different areas of data science and do not directly pertain to the evaluation of model performance in a straightforward manner like data splitting does.