Task
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4022

Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4022

Task 108062 Supervised Regression QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4022 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-CHEMBL4022
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.4592, mean_prior_absolute_error: 0.5121, number_of_instances: 13, relative_absolute_error: 0.8968, root_mean_prior_squared_error: 0.5695, root_mean_squared_error: 0.6112, root_relative_squared_error: 1.0733,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.rpart(3) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4022
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5369, mean_prior_absolute_error: 0.5121, number_of_instances: 13, relative_absolute_error: 1.0484, root_mean_prior_squared_error: 0.5695, root_mean_squared_error: 0.6031, root_relative_squared_error: 1.0591,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.ksvm(2) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4022
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.6282, mean_prior_absolute_error: 0.5121, number_of_instances: 13, relative_absolute_error: 1.2267, root_mean_prior_squared_error: 0.5695, root_mean_squared_error: 0.7016, root_relative_squared_error: 1.232,

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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-CHEMBL4022 (13)
    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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