Skip to main content

Table 3 In each run, a random combination of hyperparameters is tested

From: Load forecasting for energy communities: a novel LSTM-XGBoost hybrid model based on smart meter data

Hyperparameter Space Distribution
N estimators [40,1000] Randint
Max depth [1, 100] Randint
Learning rate [0.01, 0.59] Uniform
Subsample [0.3, 0.6] Uniform
Colsample bytree [0.5, 0.4] Uniform
Min child weight [0.05, 0.1, 0.02,1, 2, 3, 4] None
Gamma [0,0.5,2,10] None
  1. The number of nestimators and the maxdepth is determined by drawing random integers. Minchildweight and gamma are determined by drawing from a set of pre-determined values. The remaining hyperparameters are drawn from uniform distributions