Run
10590816

Run 10590816

Task 59 (Supervised Classification) iris 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_state17773
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

Area under ROC curve
0.9979 ± 0.0042
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.96 ± 0.048
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.94 ± 0.0699
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8683 ± 0.0429
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.08 ± 0.0217
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4444
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.96 ± 0.0466
Cross-validation details (10-fold Crossvalidation)
Number of instances
150
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.96 ± 0.0355
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.96 ± 0.0466
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.585
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.18 ± 0.0489
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4714
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1491 ± 0.0392
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3162 ± 0.0831
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.96 ± 0.0466
Cross-validation details (10-fold Crossvalidation)