Task
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3669

Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3669

Task 108309 Supervised Regression QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3669 3 runs submitted
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Noureddin Sadawi 8 years ago
ran flow mlr.regr.rsm(2) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3669
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.4032, mean_prior_absolute_error: 0.226, number_of_instances: 17, relative_absolute_error: 1.7836, root_mean_prior_squared_error: 0.2584, root_mean_squared_error: 0.4677, root_relative_squared_error: 1.8101,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.rpart(3) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3669
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.2389, mean_prior_absolute_error: 0.226, number_of_instances: 17, relative_absolute_error: 1.0571, root_mean_prior_squared_error: 0.2584, root_mean_squared_error: 0.2784, root_relative_squared_error: 1.0775,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.ksvm(2) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3669
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.1688, mean_prior_absolute_error: 0.226, number_of_instances: 17, relative_absolute_error: 0.7467, root_mean_prior_squared_error: 0.2584, root_mean_squared_error: 0.2152, root_relative_squared_error: 0.833,

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    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-CHEMBL3669 (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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