Task
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2939

Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2939

Task 106180 Supervised Regression QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2939 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-CHEMBL2939
0 likes - 0 downloads - 0 reach - mean_absolute_error: 167.5002, mean_prior_absolute_error: 0.4007, number_of_instances: 843, relative_absolute_error: 418.0395, root_mean_prior_squared_error: 0.6861, root_mean_squared_error: 581.6059, root_relative_squared_error: 847.6564,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.rpart(3) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2939
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.2296, mean_prior_absolute_error: 0.4007, number_of_instances: 843, relative_absolute_error: 0.5731, root_mean_prior_squared_error: 0.6861, root_mean_squared_error: 0.4495, root_relative_squared_error: 0.655,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.ksvm(2) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL2939
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.2028, mean_prior_absolute_error: 0.4007, number_of_instances: 843, relative_absolute_error: 0.506, root_mean_prior_squared_error: 0.6861, root_mean_squared_error: 0.3892, root_relative_squared_error: 0.5672,

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    Note: The leaderboard ignores resubmissions of previous solutions, as well as parameter variations that do not improve performance.

    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-CHEMBL2939 (10)
    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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