Run
10560185

Run 10560185

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

sklearn.pipeline.Pipeline(polynomialfeatures=sklearn.preprocessing.data.Pol ynomialFeatures,minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,pca=sk learn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest. RandomForestClassifier)(2)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.preprocessing.data.MinMaxScaler(7)_copytrue
sklearn.preprocessing.data.MinMaxScaler(7)_feature_range[0, 1]
sklearn.preprocessing.data.PolynomialFeatures(6)_degree2
sklearn.preprocessing.data.PolynomialFeatures(6)_include_biastrue
sklearn.preprocessing.data.PolynomialFeatures(6)_interaction_onlyfalse
sklearn.decomposition.pca.PCA(11)_copytrue
sklearn.decomposition.pca.PCA(11)_iterated_power"auto"
sklearn.decomposition.pca.PCA(11)_n_componentsnull
sklearn.decomposition.pca.PCA(11)_random_state41455
sklearn.decomposition.pca.PCA(11)_svd_solver"auto"
sklearn.decomposition.pca.PCA(11)_tol0.0
sklearn.decomposition.pca.PCA(11)_whitenfalse
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_state41434
sklearn.ensemble.forest.RandomForestClassifier(67)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(67)_warm_startfalse
sklearn.pipeline.Pipeline(polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "polynomialfeatures", "step_name": "polynomialfeatures"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "minmaxscaler", "step_name": "minmaxscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "pca", "step_name": "pca"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]

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

0.6567 ± 0.0902
Per class
Cross-validation details (10-fold Crossvalidation)
0.6303 ± 0.0593
Per class
Cross-validation details (10-fold Crossvalidation)
0.2062 ± 0.1316
Cross-validation details (10-fold Crossvalidation)
-0.0559 ± 0.0792
Cross-validation details (10-fold Crossvalidation)
0.4518 ± 0.0295
Cross-validation details (10-fold Crossvalidation)
0.4472 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
0.6218 ± 0.0594
Cross-validation details (10-fold Crossvalidation)
1055
Per class
Cross-validation details (10-fold Crossvalidation)
0.6485 ± 0.0618
Per class
Cross-validation details (10-fold Crossvalidation)
0.6218 ± 0.0594
Cross-validation details (10-fold Crossvalidation)
0.9223 ± 0.0036
Cross-validation details (10-fold Crossvalidation)
1.0101 ± 0.0649
Cross-validation details (10-fold Crossvalidation)
0.4728 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
0.4918 ± 0.0287
Cross-validation details (10-fold Crossvalidation)
1.0401 ± 0.0597
Cross-validation details (10-fold Crossvalidation)
0.6098 ± 0.0703
Cross-validation details (10-fold Crossvalidation)