Task
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3881

Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3881

Task 105955 Supervised Regression QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3881 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-CHEMBL3881
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.8869, mean_prior_absolute_error: 0.8564, number_of_instances: 37, relative_absolute_error: 1.0356, root_mean_prior_squared_error: 1.0323, root_mean_squared_error: 1.1865, root_relative_squared_error: 1.1493,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.rpart(3) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3881
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5931, mean_prior_absolute_error: 0.8564, number_of_instances: 37, relative_absolute_error: 0.6926, root_mean_prior_squared_error: 1.0323, root_mean_squared_error: 0.7555, root_relative_squared_error: 0.7318,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.ksvm(2) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL3881
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5132, mean_prior_absolute_error: 0.8564, number_of_instances: 37, relative_absolute_error: 0.5993, root_mean_prior_squared_error: 1.0323, root_mean_squared_error: 0.7155, root_relative_squared_error: 0.6931,

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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-CHEMBL3881 (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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