What is the prediction set in a dataset?

The prediction set in a dataset refers to the new data on which predictions are made. This set is crucial because it represents the real-world scenarios that the model has not encountered during the training phase. When a model is developed, it is trained on historical data, which helps it learn patterns and relationships within that data. However, the actual effectiveness of the model is assessed when it is applied to the prediction set. The insights generated from this new data can validate the performance of the model and demonstrate its accuracy and reliability in making predictions on unseen information.

In contrast, historical data used to train the model is foundational for building the predictive capabilities but does not belong to the prediction set. Additionally, data excluded from analysis or data containing errors has its own implications for data processing and model training but does not define what the prediction set entails. The prediction set is specifically focused on new inputs that the model will utilize to generate forecasts or insights, making it a vital part of the predictive analytics process.