Run
10588997

Run 10588997

Task 146065 (Supervised Classification) monks-problems-2 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.ensemble._forest.RandomForestClassifier)(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.ensemble._forest.RandomForestClassifier(12)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(12)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(12)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(12)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(12)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(12)_max_features"auto"
sklearn.ensemble._forest.RandomForestClassifier(12)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(12)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(12)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(12)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(12)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(12)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(12)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(12)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(12)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(12)_random_state0
sklearn.ensemble._forest.RandomForestClassifier(12)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(12)_warm_startfalse
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.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.ensemble._forest.RandomForestClassifier)(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.ensemble._forest.RandomForestClassifier)(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

Area under ROC curve
0.9975 ± 0.0063
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.975 ± 0.0227
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9443 ± 0.0508
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.6747 ± 0.051
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1721 ± 0.022
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4507 ± 0.0026
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.975 ± 0.0225
Cross-validation details (10-fold Crossvalidation)
Number of instances
601
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.975 ± 0.0221
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.975 ± 0.0225
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9274 ± 0.0078
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3818 ± 0.0492
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4746 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2235 ± 0.0252
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4709 ± 0.0537
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9694 ± 0.0282
Cross-validation details (10-fold Crossvalidation)