Run
10590456

Run 10590456

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 11-10-2022 by VAIBHAV JAISWAL
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Flow

sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklea rn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardSc aler),model=sklearn.linear_model._logistic.LogisticRegression)(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.linear_model._logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.linear_model._logistic.LogisticRegression)(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.linear_model._logistic.LogisticRegression)(1)_verbosefalse
sklearn.linear_model._logistic.LogisticRegression(7)_C1.0
sklearn.linear_model._logistic.LogisticRegression(7)_class_weightnull
sklearn.linear_model._logistic.LogisticRegression(7)_dualfalse
sklearn.linear_model._logistic.LogisticRegression(7)_fit_intercepttrue
sklearn.linear_model._logistic.LogisticRegression(7)_intercept_scaling1
sklearn.linear_model._logistic.LogisticRegression(7)_l1_rationull
sklearn.linear_model._logistic.LogisticRegression(7)_max_iter5000
sklearn.linear_model._logistic.LogisticRegression(7)_multi_class"auto"
sklearn.linear_model._logistic.LogisticRegression(7)_n_jobsnull
sklearn.linear_model._logistic.LogisticRegression(7)_penalty"l2"
sklearn.linear_model._logistic.LogisticRegression(7)_random_state7796
sklearn.linear_model._logistic.LogisticRegression(7)_solver"lbfgs"
sklearn.linear_model._logistic.LogisticRegression(7)_tol0.0001
sklearn.linear_model._logistic.LogisticRegression(7)_verbose0
sklearn.linear_model._logistic.LogisticRegression(7)_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

0.6194 ± 0.0172
Per class
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.590.60.610.620.630.640.650.660…0.67
0.5773 ± 0.0198
Per class
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.540.560.580.60.620.64
0.1526 ± 0.0374
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.0750.10.1250.150.1750.20.2250.…0.25
0.066 ± 0.0112
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.050.060.070.080.090.1
0.4667 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.4550.460.4650.470.450.4…0.475
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.4…0.49470.4…0.49480.4…0.4946750.4…0.4947250.4…0.494750.4…0.4947750.4…0.494825
0.5938 ± 0.0168
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.560.580.60.620.64
14980
Per class
Cross-validation details (10-fold Crossvalidation)
0.589 ± 0.0185
Per class
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.560.580.60.620.64
0.5938 ± 0.0168
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.560.580.60.620.64
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.992350.99240.992450.99250.992550.99260.99265
0.9433 ± 0.0092
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.920.930.940.950.910.96
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.4974
0.4828 ± 0.0026
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.4760.4780.480.4820.4840.4860.…0.488
0.9706 ± 0.0051
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.9550.960.9650.970.9750.98
0.5739 ± 0.0184
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.540.560.580.60.620.64
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