Task
Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1995

Supervised Regression on QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1995

Task 95601 Supervised Regression QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1995 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-CHEMBL1995
0 likes - 0 downloads - 0 reach - mean_absolute_error: 2.3479, mean_prior_absolute_error: 0.7764, number_of_instances: 393, relative_absolute_error: 3.024, root_mean_prior_squared_error: 0.9271, root_mean_squared_error: 5.1965, root_relative_squared_error: 5.6049,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.rpart(3) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1995
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.5461, mean_prior_absolute_error: 0.7764, number_of_instances: 393, relative_absolute_error: 0.7033, root_mean_prior_squared_error: 0.9271, root_mean_squared_error: 0.7547, root_relative_squared_error: 0.814,
Noureddin Sadawi 8 years ago
ran flow mlr.regr.ksvm(2) on task Supervised Regression on data set QSAR-DATASET-FOR-DRUG-TARGET-CHEMBL1995
0 likes - 0 downloads - 0 reach - mean_absolute_error: 0.4601, mean_prior_absolute_error: 0.7764, number_of_instances: 393, relative_absolute_error: 0.5926, root_mean_prior_squared_error: 0.9271, root_mean_squared_error: 0.6389, root_relative_squared_error: 0.6891,

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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-CHEMBL1995 (8)
    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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