Run
10459365

Run 10459365

Task 167140 (Supervised Classification) dna Uploaded 20-05-2020 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestcla ssifier=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 transformers in the pipeline can be cached using ``memory`` argument. 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 it to 'passthrough' or ``None``.
sklearn.impute._base.SimpleImputer(11)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(11)_copytrue
sklearn.impute._base.SimpleImputer(11)_fill_valuenull
sklearn.impute._base.SimpleImputer(11)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(11)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(11)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(16)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(16)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(16)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_sparsetrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_verbosefalse
sklearn.ensemble.forest.RandomForestClassifier(64)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(64)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(64)_criterion"entropy"
sklearn.ensemble.forest.RandomForestClassifier(64)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(64)_max_features0.9196933972221343
sklearn.ensemble.forest.RandomForestClassifier(64)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(64)_min_impurity_decrease0
sklearn.ensemble.forest.RandomForestClassifier(64)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(64)_min_samples_leaf19
sklearn.ensemble.forest.RandomForestClassifier(64)_min_samples_split18
sklearn.ensemble.forest.RandomForestClassifier(64)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(64)_n_estimators100
sklearn.ensemble.forest.RandomForestClassifier(64)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(64)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(64)_random_state1
sklearn.ensemble.forest.RandomForestClassifier(64)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(64)_warm_startfalse

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
0.9797 ± 0.0066
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9091 ± 0.012
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8524 ± 0.0193
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8051 ± 0.0156
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0988 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.41 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.908 ± 0.0122
Cross-validation details (10-fold Crossvalidation)
Number of instances
3186
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9118 ± 0.0114
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.908 ± 0.0122
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.4798 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2411 ± 0.0153
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4527 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2131 ± 0.0146
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4706 ± 0.0322
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9052 ± 0.0155
Cross-validation details (10-fold Crossvalidation)