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Showing 1 to 2 of 2 for “"forest interpretation"”.

  1. Random forests and their application to heteroscedastic drug design data

    Random forests are a popular machine learning method that make predictions by ensembling decision trees. They are widely used on tabular data, particularly drug design datasets. Whilst they often give good predictions they are difficult to interpret and the reasons for their successes and failures …

    cambridge Repository record for Random forests and their application to heteroscedastic drug design data (opens in a new tab)