Run
10560241

Run 10560241

Task 167141 (Supervised Classification) churn Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.Standar dScaler,mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClass ifier)(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 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 to None.
sklearn.preprocessing.data.StandardScaler(43)_copytrue
sklearn.preprocessing.data.StandardScaler(43)_with_meantrue
sklearn.preprocessing.data.StandardScaler(43)_with_stdtrue
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "mlpclassifier", "step_name": "mlpclassifier"}}]
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_activation"relu"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_alpha0.0001
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_batch_size"auto"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_beta_10.9
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_beta_20.999
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_early_stoppingfalse
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_epsilon1e-08
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_hidden_layer_sizes[100, 100, 100]
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_learning_rate"invscaling"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_learning_rate_init0.003
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_max_iter500
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_momentum0.9
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_nesterovs_momentumtrue
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_power_t0.5
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_random_state3
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_shuffletrue
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_solver"adam"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_tol0.0001
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_validation_fraction0.1
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_verbosefalse
sklearn.neural_network.multilayer_perceptron.MLPClassifier(19)_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
0.8901 ± 0.0253
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9272 ± 0.0094
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.692 ± 0.0437
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6117 ± 0.0577
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0751 ± 0.01
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2429 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9292 ± 0.0089
Cross-validation details (10-fold Crossvalidation)
Number of instances
5000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9263 ± 0.0091
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9292 ± 0.0089
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.5879 ± 0.0025
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3094 ± 0.0417
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3484 ± 0.001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2496 ± 0.0141
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7164 ± 0.0416
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.8276 ± 0.0351
Cross-validation details (10-fold Crossvalidation)