Task
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1293271

Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1293271

Task 98026 Supervised Regression QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1293271 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-CHEMBL1293271
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.8803, mean_prior_absolute_error: 0.3872, number_of_instances: 27, relative_absolute_error: 2.2736, root_mean_prior_squared_error: 0.4652, root_mean_squared_error: 1.1411, root_relative_squared_error: 2.453,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.rpart(3) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1293271
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.4161, mean_prior_absolute_error: 0.3872, number_of_instances: 27, relative_absolute_error: 1.0747, root_mean_prior_squared_error: 0.4652, root_mean_squared_error: 0.5002, root_relative_squared_error: 1.0752,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.ksvm(2) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1293271
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.4274, mean_prior_absolute_error: 0.3872, number_of_instances: 27, relative_absolute_error: 1.104, root_mean_prior_squared_error: 0.4652, root_mean_squared_error: 0.5122, root_relative_squared_error: 1.1011,

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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-CHEMBL1293271 (9)
    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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