Task
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1958

Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1958

Task 94065 Supervised Regression QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1958 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-CHEMBL1958
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.9924, mean_prior_absolute_error: 0.6966, number_of_instances: 15, relative_absolute_error: 1.4246, root_mean_prior_squared_error: 0.8789, root_mean_squared_error: 1.1707, root_relative_squared_error: 1.3319,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.rpart(3) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1958
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.7582, mean_prior_absolute_error: 0.6966, number_of_instances: 15, relative_absolute_error: 1.0884, root_mean_prior_squared_error: 0.8789, root_mean_squared_error: 0.953, root_relative_squared_error: 1.0842,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.ksvm(2) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1958
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.7806, mean_prior_absolute_error: 0.6966, number_of_instances: 15, relative_absolute_error: 1.1205, root_mean_prior_squared_error: 0.8789, root_mean_squared_error: 0.9349, root_relative_squared_error: 1.0637,

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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-CHEMBL1958 (7)
    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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