Hedged random forest method “consistently outperforms” unhedged approach
Swiss National Bank (SNB) researchers say they have managed to improve upon the already popular “random forest” machine learning-based inflation prediction model.
In their working paper, published on June 4, Eliot Beck and Michael Wolf show that by assigning “non-equal (and even negative weights) of the individual trees” to random forest models, inflation forecasts can be improved by around 5% in terms of the root mean-squared error (RMSE) and 6% in terms of the mean absolute error (MAE).
Beck and
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