Task
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4525

Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4525

Task 94193 Supervised Regression QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4525 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-CHEMBL4525
0 likes - 0 downloads - 0 reach - mean_absolute_error: 159941208918.66, mean_prior_absolute_error: 0.6293, number_of_instances: 655, relative_absolute_error: 254137519797.03, root_mean_prior_squared_error: 0.7827, root_mean_squared_error: 1128412792077.2, root_relative_squared_error: 1441608937941.3,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.rpart(3) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4525
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.4318, mean_prior_absolute_error: 0.6293, number_of_instances: 655, relative_absolute_error: 0.6861, root_mean_prior_squared_error: 0.7827, root_mean_squared_error: 0.6573, root_relative_squared_error: 0.8397,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.ksvm(2) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4525
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.4025, mean_prior_absolute_error: 0.6293, number_of_instances: 655, relative_absolute_error: 0.6395, root_mean_prior_squared_error: 0.7827, root_mean_squared_error: 0.5981, root_relative_squared_error: 0.7642,

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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-CHEMBL4525 (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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