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In machine learning and data science, "true risk" refers to the actual or inherent risk associated with a predictive model's performance when applied to new, unseen data. It is also sometimes referred to as "population risk" or "Bayes risk."

True risk is a theoretical concept that represents how well a machine learning model is expected to perform on a broad and representative dataset from the same population that the model was trained on. It takes into account the model's ability to generalize from the training data to make accurate predictions on new, previously unseen examples.

The true risk of a model is not directly observable because it requires knowledge of the entire population of data, which is typically not available in practice. Instead, machine learning practitioners use various techniques, such as cross-validation and hypothesis testing, to estimate a model's performance on unseen data based on the available training and validation datasets.

The goal in machine learning is to create models that have low true risk, indicating that they generalize well to new data and make accurate predictions. However, achieving low true risk can be challenging, as models can overfit to the training data (perform well on the training data but poorly on new data) or underfit (perform poorly on both the training and new data). Therefore, model evaluation, selection, and tuning are essential steps in minimizing the true risk of machine learning models.

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Table 3953. Application examples of "true risk".