Task
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5026

Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5026

Task 103755 Supervised Regression QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5026 3 runs submitted
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Noureddin Sadawi 9 years ago
ran flow mlr.regr.rsm(2) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5026
0 likes - 0 downloads - 0 reach - mean_absolute_error: 252.0518, mean_prior_absolute_error: 0.1293, number_of_instances: 445, relative_absolute_error: 1949.8278, root_mean_prior_squared_error: 0.2628, root_mean_squared_error: 978.7455, root_relative_squared_error: 3724.4147,
Noureddin Sadawi 9 years ago
ran flow mlr.regr.rpart(3) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5026
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.1045, mean_prior_absolute_error: 0.1293, number_of_instances: 445, relative_absolute_error: 0.8085, root_mean_prior_squared_error: 0.2628, root_mean_squared_error: 0.2641, root_relative_squared_error: 1.0051,
Noureddin Sadawi 9 years ago
ran flow mlr.regr.ksvm(2) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5026
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.1263, mean_prior_absolute_error: 0.1293, number_of_instances: 445, relative_absolute_error: 0.9772, root_mean_prior_squared_error: 0.2628, root_mean_squared_error: 0.2561, root_relative_squared_error: 0.9747,

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    Note: The leaderboard ignores resubmissions of previous solutions, as well as parameter variations that do not improve performance.

    Challenge

    Given a dataset with a numeric target and a set of train/test splits, e.g. generated by a cross-validation procedure, train a model and return the predictions of that model.

    Given inputs

    estimation_procedure Custom 10-fold Crossvalidation
    source_data QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5026 (12)
    target_feature pXC50

    Expected outputs

    evaluations A list of user-defined evaluations of the task as key-value pairs. KeyValue (optional)
    model A file containing the model built on all the input data. File (optional)
    predictions The desired output format Predictions (optional)

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