Run
10593666

Run 10593666

Task 361444 (Supervised Classification) phoneme Uploaded 09-05-2023 by Takeaki Sakabe
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.linear_model._logistic.LogisticRegressionCV)(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(43)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(43)_copytrue
sklearn.impute._base.SimpleImputer(43)_fill_valuenull
sklearn.impute._base.SimpleImputer(43)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(43)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(43)_strategy"mean"
sklearn.impute._base.SimpleImputer(43)_verbose"deprecated"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._logistic.LogisticRegressionCV)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._logistic.LogisticRegressionCV)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._logistic.LogisticRegressionCV)(1)_verbosefalse
sklearn.linear_model._logistic.LogisticRegressionCV(1)_Cs10
sklearn.linear_model._logistic.LogisticRegressionCV(1)_class_weightnull
sklearn.linear_model._logistic.LogisticRegressionCV(1)_cvnull
sklearn.linear_model._logistic.LogisticRegressionCV(1)_dualfalse
sklearn.linear_model._logistic.LogisticRegressionCV(1)_fit_intercepttrue
sklearn.linear_model._logistic.LogisticRegressionCV(1)_intercept_scaling1.0
sklearn.linear_model._logistic.LogisticRegressionCV(1)_l1_ratiosnull
sklearn.linear_model._logistic.LogisticRegressionCV(1)_max_iter100
sklearn.linear_model._logistic.LogisticRegressionCV(1)_multi_class"auto"
sklearn.linear_model._logistic.LogisticRegressionCV(1)_n_jobsnull
sklearn.linear_model._logistic.LogisticRegressionCV(1)_penalty"l2"
sklearn.linear_model._logistic.LogisticRegressionCV(1)_random_state61189
sklearn.linear_model._logistic.LogisticRegressionCV(1)_refittrue
sklearn.linear_model._logistic.LogisticRegressionCV(1)_scoringnull
sklearn.linear_model._logistic.LogisticRegressionCV(1)_solver"lbfgs"
sklearn.linear_model._logistic.LogisticRegressionCV(1)_tol0.0001
sklearn.linear_model._logistic.LogisticRegressionCV(1)_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
0.8122 ± 0.0046
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
F measure
0.7396 ± 0.0046
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
Kappa
0.3547 ± 0.0119
Cross-validation details (5 times 2-fold Crossvalidation)
KB relative information score
0.2376 ± 0.0054
Cross-validation details (5 times 2-fold Crossvalidation)
Mean absolute error
0.3121 ± 0.0021
Cross-validation details (5 times 2-fold Crossvalidation)
Mean prior absolute error
0.4147 ± 0
Cross-validation details (5 times 2-fold Crossvalidation)
Weighted recall
0.7496 ± 0.0041
Cross-validation details (5 times 2-fold Crossvalidation)
Number of instances
27020
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
Precision
0.7366 ± 0.0047
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
Predictive accuracy
0.7496 ± 0.0041
Cross-validation details (5 times 2-fold Crossvalidation)
Prior entropy
0.8732 ± 0
Cross-validation details (5 times 2-fold Crossvalidation)
Relative absolute error
0.7524 ± 0.0052
Cross-validation details (5 times 2-fold Crossvalidation)
Root mean prior squared error
0.4554 ± 0
Cross-validation details (5 times 2-fold Crossvalidation)
Root mean squared error
0.3975 ± 0.0021
Cross-validation details (5 times 2-fold Crossvalidation)
Root relative squared error
0.8729 ± 0.0045
Cross-validation details (5 times 2-fold Crossvalidation)
Unweighted recall
0.6659 ± 0.0062
Cross-validation details (5 times 2-fold Crossvalidation)