Prediction Intervals
Prediction intervals are a statistical concept used to estimate the range within which future observations are likely to fall, given a certain level of confidence. They account for both the uncertainty in the model's parameters and the inherent variability in the data, providing a more realistic measure of prediction uncertainty than point estimates alone. This is commonly applied in regression analysis, time series forecasting, and machine learning to quantify the reliability of predictions.
Developers should learn prediction intervals when building predictive models in fields like finance, healthcare, or supply chain management, where understanding the uncertainty of forecasts is critical for risk assessment and decision-making. They are essential in machine learning for model evaluation, helping to set realistic expectations and improve trust in AI systems by providing confidence bounds around predictions.