Run
10591079

Run 10591079

Task 34539 (Supervised Classification) Amazon_employee_access Uploaded 11-10-2022 by VAIBHAV JAISWAL
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklea rn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardSc aler),model=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.preprocessing._data.StandardScaler(11)_copytrue
sklearn.preprocessing._data.StandardScaler(11)_with_meantrue
sklearn.preprocessing._data.StandardScaler(11)_with_stdtrue
sklearn.impute._base.SimpleImputer(30)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(30)_copytrue
sklearn.impute._base.SimpleImputer(30)_fill_valuenull
sklearn.impute._base.SimpleImputer(30)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(30)_strategy"mean"
sklearn.impute._base.SimpleImputer(30)_verbose0
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler)(2)_memorynull
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}]
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler)(2)_verbosefalse
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.neural_network._multilayer_perceptron.MLPClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.neural_network._multilayer_perceptron.MLPClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "numerical", "step_name": "numerical"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "model", "step_name": "model"}}]
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.neural_network._multilayer_perceptron.MLPClassifier)(1)_verbosefalse
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_activation"relu"
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_alpha0.0001
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_batch_size"auto"
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_beta_10.9
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_beta_20.999
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_early_stoppingfalse
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_epsilon1e-08
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_hidden_layer_sizes[100]
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_learning_rate"constant"
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_learning_rate_init0.001
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_max_fun15000
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_max_iter5000
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_momentum0.9
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_n_iter_no_change10
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_nesterovs_momentumtrue
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_power_t0.5
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_random_state50171
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_shuffletrue
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_solver"adam"
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_tol0.0001
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_validation_fraction0.1
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_verbosefalse
sklearn.neural_network._multilayer_perceptron.MLPClassifier(1)_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
0.6668 ± 0.0205
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9147 ± 0.0007
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0108 ± 0.0102
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.4402 ± 0.0957
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1048 ± 0.0033
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1091 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.942 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Number of instances
32769
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9127 ± 0.0219
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.942 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.319 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9609 ± 0.0305
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2335 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2304 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9868 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5029 ± 0.0028
Cross-validation details (10-fold Crossvalidation)