Task
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5903

Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5903

Task 103611 Supervised Regression QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5903 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-CHEMBL5903
0 likes - 0 downloads - 0 reach - mean_absolute_error: 1281823963292.4, mean_prior_absolute_error: 0.4906, number_of_instances: 399, relative_absolute_error: 2612821174812.6, root_mean_prior_squared_error: 0.7152, root_mean_squared_error: 6474411482194.4, root_relative_squared_error: 9053107301198.4,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.rpart(3) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5903
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5263, mean_prior_absolute_error: 0.4906, number_of_instances: 399, relative_absolute_error: 1.0727, root_mean_prior_squared_error: 0.7152, root_mean_squared_error: 0.7675, root_relative_squared_error: 1.0731,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.ksvm(2) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL5903
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.4501, mean_prior_absolute_error: 0.4906, number_of_instances: 399, relative_absolute_error: 0.9175, root_mean_prior_squared_error: 0.7152, root_mean_squared_error: 0.671, root_relative_squared_error: 0.9383,

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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-CHEMBL5903 (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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