Run
10593660

Run 10593660

Task 361347 (Supervised Classification) mnist_784 Uploaded 09-05-2023 by Takeaki Sakabe
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.neural_network._multilayer_perceptron.MLPClassifier)(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(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.neural_network._multilayer_perceptron.MLPClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neural_network._multilayer_perceptron.MLPClassifier)(2)_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)(2)_verbosefalse
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_activation"relu"
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_alpha0.0001
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_batch_size"auto"
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_beta_10.9
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_beta_20.999
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_early_stoppingfalse
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_epsilon1e-08
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_hidden_layer_sizes[10]
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_learning_rate"constant"
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_learning_rate_init0.001
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_max_fun15000
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_max_iter200
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_momentum0.9
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_n_iter_no_change10
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_nesterovs_momentumtrue
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_power_t0.5
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_random_state60201
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_shuffletrue
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_solver"adam"
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_tol0.0001
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_validation_fraction0.1
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_verbosefalse
sklearn.neural_network._multilayer_perceptron.MLPClassifier(4)_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
0.9725 ± 0.0063
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
F measure
0.8112 ± 0.0337
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
Kappa
0.7919 ± 0.0364
Cross-validation details (5 times 2-fold Crossvalidation)
KB relative information score
0.7866 ± 0.0379
Cross-validation details (5 times 2-fold Crossvalidation)
Mean absolute error
0.0558 ± 0.0103
Cross-validation details (5 times 2-fold Crossvalidation)
Mean prior absolute error
0.1799 ± 0
Cross-validation details (5 times 2-fold Crossvalidation)
Weighted recall
0.8128 ± 0.0327
Cross-validation details (5 times 2-fold Crossvalidation)
Number of instances
350000
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
Precision
0.8108 ± 0.0333
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
Predictive accuracy
0.8128 ± 0.0327
Cross-validation details (5 times 2-fold Crossvalidation)
Prior entropy
3.3198 ± 0
Cross-validation details (5 times 2-fold Crossvalidation)
Relative absolute error
0.3099 ± 0.0572
Cross-validation details (5 times 2-fold Crossvalidation)
Root mean prior squared error
0.3 ± 0
Cross-validation details (5 times 2-fold Crossvalidation)
Root mean squared error
0.1678 ± 0.014
Cross-validation details (5 times 2-fold Crossvalidation)
Root relative squared error
0.5594 ± 0.0468
Cross-validation details (5 times 2-fold Crossvalidation)
Unweighted recall
0.81 ± 0.0335
Cross-validation details (5 times 2-fold Crossvalidation)