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Technical GlossaryMachine Learning

Quantile Regression

A regression approach that models specific quantiles of the target variable rather than only its mean.

Quantile regression allows modeling different parts of the target distribution instead of focusing only on the mean trend. This makes it possible to analyze lower, median, or upper-risk regions separately. It is particularly valuable in finance, demand forecasting, risk modeling, and income-distribution analysis. In datasets with heteroskedasticity, it can provide richer insight than standard regression methods.

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Mean Squared Error (MSE)

A common regression loss function that averages the squared differences between predictions and true values.

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Mean Absolute Error (MAE)

A regression loss function that averages the absolute differences between predictions and true values, offering greater robustness.

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Huber Loss

A hybrid loss function that balances MSE and MAE behavior and is more robust to outliers.