Run
10465686

Run 10465686

Task 3 (Supervised Classification) kr-vs-kp Uploaded 04-08-2020 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,logisticregress ion=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.impute._base.SimpleImputer(16)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(16)_copytrue
sklearn.impute._base.SimpleImputer(16)_fill_valuenull
sklearn.impute._base.SimpleImputer(16)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(16)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(16)_verbose0
sklearn.linear_model._logistic.LogisticRegression(3)_C12740.8111607
sklearn.linear_model._logistic.LogisticRegression(3)_class_weightnull
sklearn.linear_model._logistic.LogisticRegression(3)_dualfalse
sklearn.linear_model._logistic.LogisticRegression(3)_fit_intercepttrue
sklearn.linear_model._logistic.LogisticRegression(3)_intercept_scaling1
sklearn.linear_model._logistic.LogisticRegression(3)_l1_rationull
sklearn.linear_model._logistic.LogisticRegression(3)_max_iter920
sklearn.linear_model._logistic.LogisticRegression(3)_multi_class"auto"
sklearn.linear_model._logistic.LogisticRegression(3)_n_jobsnull
sklearn.linear_model._logistic.LogisticRegression(3)_penalty"l2"
sklearn.linear_model._logistic.LogisticRegression(3)_random_state1
sklearn.linear_model._logistic.LogisticRegression(3)_solver"liblinear"
sklearn.linear_model._logistic.LogisticRegression(3)_tol0.0001
sklearn.linear_model._logistic.LogisticRegression(3)_verbose0
sklearn.linear_model._logistic.LogisticRegression(3)_warm_startfalse
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,logisticregression=sklearn.linear_model._logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,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": "onehotencoder", "step_name": "onehotencoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "logisticregression", "step_name": "logisticregression"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,logisticregression=sklearn.linear_model._logistic.LogisticRegression)(1)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(21)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(21)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(21)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(21)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(21)_sparsetrue

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
0.9956 ± 0.0028
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9743 ± 0.0119
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9486 ± 0.0238
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9201 ± 0.0205
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0428 ± 0.0102
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.499 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9743 ± 0.0119
Cross-validation details (10-fold Crossvalidation)
Number of instances
3196
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9744 ± 0.0118
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9743 ± 0.0119
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9986 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0858 ± 0.0203
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4995 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.151 ± 0.029
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3022 ± 0.058
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9744 ± 0.0119
Cross-validation details (10-fold Crossvalidation)