Which technique involves using records with similar predictor values for prediction purposes?

The nearest neighbor method is a technique used in predictive analytics and machine learning where the algorithm identifies data points that are similar based on their predictor values to make predictions. This method operates on the principle that similar data points share similar outcomes, making it particularly useful for classification and regression tasks.

In practice, when using this method, you look at the 'neighbors' or records in a dataset that closely resemble the input data based on defined attributes or features. By identifying these neighbors, the method can predict the outcome for a new input by examining the outcomes of the similar records. This approach is especially effective in situations where the relationships in the data are too complex for simpler models to capture accurately, as it leverages the existing records for improved accuracy.

Cluster analysis, on the other hand, groups data into clusters based on similarity without making predictions. Regression analysis focuses on determining the relationship between variables to predict outcomes rather than specifically identifying records with similar predictor values. Forecasting techniques generally rely on historical data to predict future trends, which does not emphasize similarity in the way the nearest neighbor method does.