What technique is commonly used to measure the performance of a predictive model?

The confusion matrix is a key technique used to evaluate the performance of a predictive model, especially in classification problems. It provides a comprehensive visualization of the model’s performance by displaying the true positives, false positives, true negatives, and false negatives. This allows for the calculation of various performance metrics such as accuracy, precision, recall, and F1-score, which are essential for understanding how well the model is performing.

The confusion matrix breaks down the model's predictions into categories, making it easier to identify where the model is succeeding and where it is misclassifying instances. By analyzing the metrics derived from the confusion matrix, data scientists can gain insight into the strengths and weaknesses of the model, ultimately guiding improvements and adjustments.

In contrast, the other options do not serve the same purpose in evaluating predictive model performance. Frequency analysis is typically used for understanding distribution and trends in data rather than assessing model effectiveness. Sample selection refers to the process of choosing a representative subset of data for analysis, which does not directly evaluate model performance. Statistical sampling involves selecting random samples from a population and, while useful in various analyses, it does not provide insights on the predictive accuracy of a model.