Run
10589152

Run 10589152

Task 10101 (Supervised Classification) blood-transfusion-service-center Uploaded 27-09-2022 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.tree._classes.DecisionTreeClassifier)(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.tree._classes.DecisionTreeClassifier(25)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(25)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(25)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(25)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(25)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(25)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(25)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(25)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(25)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(25)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(25)_random_state0
sklearn.tree._classes.DecisionTreeClassifier(25)_splitter"best"
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.tree._classes.DecisionTreeClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.tree._classes.DecisionTreeClassifier)(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.tree._classes.DecisionTreeClassifier)(1)_verbosefalse

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

0.5882 ± 0.0443
Per class
Cross-validation details (10-fold Crossvalidation)
0.7029 ± 0.0552
Per class
Cross-validation details (10-fold Crossvalidation)
0.1557 ± 0.1505
Cross-validation details (10-fold Crossvalidation)
0.0523 ± 0.1409
Cross-validation details (10-fold Crossvalidation)
0.3094 ± 0.0457
Cross-validation details (10-fold Crossvalidation)
0.363 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
0.7139 ± 0.0569
Cross-validation details (10-fold Crossvalidation)
748
Per class
Cross-validation details (10-fold Crossvalidation)
0.6948 ± 0.0565
Per class
Cross-validation details (10-fold Crossvalidation)
0.7139 ± 0.0569
Cross-validation details (10-fold Crossvalidation)
0.7916 ± 0.0072
Cross-validation details (10-fold Crossvalidation)
0.8521 ± 0.1246
Cross-validation details (10-fold Crossvalidation)
0.4258 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
0.521 ± 0.0437
Cross-validation details (10-fold Crossvalidation)
1.2234 ± 0.1021
Cross-validation details (10-fold Crossvalidation)
0.5727 ± 0.072
Cross-validation details (10-fold Crossvalidation)