Run
10559034

Run 10559034

Task 146824 (Supervised Classification) mfeat-pixel Uploaded 11-08-2020 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression =sklearn.linear_model.logistic.LogisticRegression)(1)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.preprocessing.data.StandardScaler(35)_copytrue
sklearn.preprocessing.data.StandardScaler(35)_with_meantrue
sklearn.preprocessing.data.StandardScaler(35)_with_stdtrue
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"median"
sklearn.impute._base.SimpleImputer(11)_verbose0
sklearn.linear_model.logistic.LogisticRegression(33)_C1
sklearn.linear_model.logistic.LogisticRegression(33)_class_weightnull
sklearn.linear_model.logistic.LogisticRegression(33)_dualfalse
sklearn.linear_model.logistic.LogisticRegression(33)_fit_intercepttrue
sklearn.linear_model.logistic.LogisticRegression(33)_intercept_scaling1
sklearn.linear_model.logistic.LogisticRegression(33)_l1_rationull
sklearn.linear_model.logistic.LogisticRegression(33)_max_iter10000
sklearn.linear_model.logistic.LogisticRegression(33)_multi_class"auto"
sklearn.linear_model.logistic.LogisticRegression(33)_n_jobsnull
sklearn.linear_model.logistic.LogisticRegression(33)_penalty"l2"
sklearn.linear_model.logistic.LogisticRegression(33)_random_state1
sklearn.linear_model.logistic.LogisticRegression(33)_solver"lbfgs"
sklearn.linear_model.logistic.LogisticRegression(33)_tol0.0001
sklearn.linear_model.logistic.LogisticRegression(33)_verbose0
sklearn.linear_model.logistic.LogisticRegression(33)_warm_startfalse
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "logisticregression", "step_name": "logisticregression"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(1)_verbosefalse

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
0.9972 ± 0.0016
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9625 ± 0.0109
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9583 ± 0.0121
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9621 ± 0.0093
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0095 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.18
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9625 ± 0.0109
Cross-validation details (10-fold Crossvalidation)
Number of instances
2000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9628 ± 0.0103
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9625 ± 0.0109
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.3219
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0528 ± 0.0108
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0738 ± 0.0096
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.2461 ± 0.032
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9625 ± 0.0109
Cross-validation details (10-fold Crossvalidation)