Task
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4577

Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4577

Task 106963 Supervised Regression QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4577 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-CHEMBL4577
0 likes - 0 downloads - 0 reach - mean_absolute_error: 2.9119, mean_prior_absolute_error: 0.6771, number_of_instances: 76, relative_absolute_error: 4.3006, root_mean_prior_squared_error: 0.8396, root_mean_squared_error: 4.0513, root_relative_squared_error: 4.8251,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.rpart(3) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4577
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.7483, mean_prior_absolute_error: 0.6771, number_of_instances: 76, relative_absolute_error: 1.1051, root_mean_prior_squared_error: 0.8396, root_mean_squared_error: 0.9655, root_relative_squared_error: 1.1499,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.ksvm(2) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL4577
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5748, mean_prior_absolute_error: 0.6771, number_of_instances: 76, relative_absolute_error: 0.849, root_mean_prior_squared_error: 0.8396, root_mean_squared_error: 0.8017, root_relative_squared_error: 0.9549,

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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-CHEMBL4577 (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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