Run
10593813

Run 10593813

Task 3 (Supervised Classification) kr-vs-kp Uploaded 25-06-2023 by Kevin Hartmann
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,encode r=sklearn.preprocessing._encoders.OneHotEncoder,scaler=sklearn.preprocessin g._data.StandardScaler,classifier=sklearn.tree._classes.DecisionTreeClassif ier)(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.impute._base.SimpleImputer(43)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(43)_copytrue
sklearn.impute._base.SimpleImputer(43)_fill_valuenull
sklearn.impute._base.SimpleImputer(43)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(43)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(43)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(43)_verbose"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(35)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(35)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(35)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(35)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(35)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(35)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(35)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(35)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(35)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(35)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(35)_random_state56422
sklearn.tree._classes.DecisionTreeClassifier(35)_splitter"best"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder,scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder,scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "encoder", "step_name": "encoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder,scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(43)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(43)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(43)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(43)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(43)_max_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(43)_min_frequencynull
sklearn.preprocessing._encoders.OneHotEncoder(43)_sparsefalse
sklearn.preprocessing._encoders.OneHotEncoder(43)_sparse_outputtrue
sklearn.preprocessing._data.StandardScaler(15)_copytrue
sklearn.preprocessing._data.StandardScaler(15)_with_meantrue
sklearn.preprocessing._data.StandardScaler(15)_with_stdtrue

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
0.9959 ± 0.003
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9959 ± 0.003
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9918 ± 0.006
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.9918 ± 0.006
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0041 ± 0.003
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.499 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9959 ± 0.003
Cross-validation details (10-fold Crossvalidation)
Number of instances
3196
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9959 ± 0.0029
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9959 ± 0.003
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9986 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.0082 ± 0.006
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4995 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0638 ± 0.0326
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.1277 ± 0.0652
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9959 ± 0.003
Cross-validation details (10-fold Crossvalidation)