Task
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1075100

Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1075100

Task 103141 Supervised Regression QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1075100 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-CHEMBL1075100
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.3913, mean_prior_absolute_error: 0.408, number_of_instances: 15, relative_absolute_error: 0.9589, root_mean_prior_squared_error: 0.5749, root_mean_squared_error: 0.4962, root_relative_squared_error: 0.8632,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.rpart(3) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1075100
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.4416, mean_prior_absolute_error: 0.408, number_of_instances: 15, relative_absolute_error: 1.0822, root_mean_prior_squared_error: 0.5749, root_mean_squared_error: 0.6191, root_relative_squared_error: 1.077,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.ksvm(2) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1075100
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.2243, mean_prior_absolute_error: 0.408, number_of_instances: 15, relative_absolute_error: 0.5496, root_mean_prior_squared_error: 0.5749, root_mean_squared_error: 0.4006, root_relative_squared_error: 0.6968,

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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-CHEMBL1075100 (11)
    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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