Run
10560178

Run 10560178

Task 9957 (Supervised Classification) qsar-biodeg Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer, randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(3)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting to None.
sklearn.ensemble.forest.RandomForestClassifier(67)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(67)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(67)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(67)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(67)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(67)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(67)_min_impurity_split1e-07
sklearn.ensemble.forest.RandomForestClassifier(67)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(67)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(67)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(67)_n_estimators10
sklearn.ensemble.forest.RandomForestClassifier(67)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(67)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(67)_random_state21890
sklearn.ensemble.forest.RandomForestClassifier(67)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(67)_warm_startfalse
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(3)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.preprocessing.imputation.Imputer(52)_axis0
sklearn.preprocessing.imputation.Imputer(52)_copytrue
sklearn.preprocessing.imputation.Imputer(52)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(52)_strategy"median"
sklearn.preprocessing.imputation.Imputer(52)_verbose0

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

18 Evaluation measures

Area under ROC curve
0.9107 ± 0.0383
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8492 ± 0.0324
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.658 ± 0.0749
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.5431 ± 0.0489
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.21 ± 0.0197
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4472 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8521 ± 0.03
Cross-validation details (10-fold Crossvalidation)
Number of instances
1055
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8504 ± 0.0299
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8521 ± 0.03
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9223 ± 0.0036
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4694 ± 0.044
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4728 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3262 ± 0.0309
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.69 ± 0.0653
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8181 ± 0.0403
Cross-validation details (10-fold Crossvalidation)