Mastering Uncertainty with CatBoost
Prediction intervals play a crucial role in regression analysis, especially when the objective extends beyond mere point predictions to assess the uncertainty or variability of the predictions. Unlike point predictions, which provide a single estimated value for each input, prediction intervals offer a range within which the true value is expected to lie with a certain probability. This is particularly valuable because it accounts for the inherent uncertainty in any predictive modeling. By quantifying this uncertainty, prediction intervals provide a more comprehensive understanding of the possible outcomes. For instance, in financial forecasting, knowing the range within which future returns might fluctuate is crucial for risk management and investment strategies.
Furthermore, the pursuit of creating the narrowest, or most "efficient," prediction intervals in a regression model enhances the precision and reliability of the model's outputs. Narrower intervals indicate a higher level of certainty in the predictions, assuming the intervals are accurate and consistently capture the true values.
In general the prediction interval is written as:
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