OpenML
10592070

Run 10592070

Task 43 (Supervised Classification) spambase Uploaded 20-03-2023 by Takeaki Sakabe
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.neural_network._multilayer_perceptron.MLPClassifier)(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(42)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(42)_copytrue
sklearn.impute._base.SimpleImputer(42)_fill_valuenull
sklearn.impute._base.SimpleImputer(42)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(42)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(42)_strategy"mean"
sklearn.impute._base.SimpleImputer(42)_verbose"deprecated"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neural_network._multilayer_perceptron.MLPClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neural_network._multilayer_perceptron.MLPClassifier)(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.neural_network._multilayer_perceptron.MLPClassifier)(1)_verbosefalse
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_activation"relu"
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_alpha0.0001
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_batch_size"auto"
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_beta_10.9
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_beta_20.999
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_early_stoppingfalse
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_epsilon1e-08
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_hidden_layer_sizes[100]
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_learning_rate"constant"
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_learning_rate_init0.001
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_max_fun15000
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_max_iter200
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_momentum0.9
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_n_iter_no_change10
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_nesterovs_momentumtrue
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_power_t0.5
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_random_state11966
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_shuffletrue
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_solver"adam"
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_tol0.0001
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_validation_fraction0.1
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_verbosefalse
sklearn.neural_network._multilayer_perceptron.MLPClassifier(3)_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
0.9722 ± 0.007
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9292 ± 0.011
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.852 ± 0.0227
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.786 ± 0.0196
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1086 ± 0.0091
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4776 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9291 ± 0.0112
Cross-validation details (10-fold Crossvalidation)
Number of instances
4601
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9294 ± 0.0104
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9291 ± 0.0112
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9674 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2273 ± 0.0191
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4886 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2376 ± 0.0182
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4862 ± 0.0374
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.927 ± 0.0104
Cross-validation details (10-fold Crossvalidation)