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Quantile Regression Forest
An extension of random forests that estimates the complete conditional distribution of the target variable, allowing prediction of any quantile, not just the mean.
Ensemble Forest
A learning method that builds multiple prediction models (usually decision trees) and combines their predictions to improve robustness and accuracy.
Conditional Quantile
The quantile of the distribution of a target variable, given a certain set of values for the predictor variables, instead of the global quantile of the distribution.
Conditional Distribution
The probability distribution of a target variable, conditioned by the observed values of the input variables, capturing the uncertainty of the prediction.
Quantile Regression
A statistical regression method that estimates the conditional quantiles of the response variable, providing a more complete view of the relationship between variables than a simple least squares regression.
Quantile Loss Function
An asymmetric loss function used in quantile regression to penalize overestimations and underestimations differently, according to the target quantile.
Ensemble Method
A machine learning technique that combines multiple base models to produce an optimal prediction, often more accurate and robust than any of the individual models.
Quantile Value
The value below which a given percentage of observations in a group of observations falls, used to divide a probability distribution into intervals of equal size.
Ensemble Prediction
The final result of an ensemble model, obtained by aggregating the predictions of several base models, for example by calculating the average or majority vote.
Empirical Distribution Function
An estimate of the cumulative distribution function of a probability distribution, calculated from a sample of data by counting the proportion of observations less than or equal to a given value.