Run
2224

Run 2224

Task 115 (Supervised Classification) diabetes Uploaded 17-10-2024 by Continuous Integration
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler ,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sk learn.tree._classes.DecisionTreeClassifier))(2)A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with a final :term:`predictor` for predictive modeling. Intermediate steps of the pipeline must be 'transforms', that is, they must implement `fit` and `transform` methods. The final :term:`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`. For an example use case of `Pipeline` combined with :class:`~s...
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier))(2)_memorynull
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier))(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "boosting", "step_name": "boosting"}}]
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier))(2)_verbosefalse
sklearn.preprocessing._data.StandardScaler(7)_copytrue
sklearn.preprocessing._data.StandardScaler(7)_with_meanfalse
sklearn.preprocessing._data.StandardScaler(7)_with_stdtrue
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier)(2)_algorithm"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier)(2)_learning_rate1.0
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier)(2)_n_estimators50
sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier)(2)_random_state49429
sklearn.tree._classes.DecisionTreeClassifier(7)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeClassifier(7)_class_weightnull
sklearn.tree._classes.DecisionTreeClassifier(7)_criterion"gini"
sklearn.tree._classes.DecisionTreeClassifier(7)_max_depthnull
sklearn.tree._classes.DecisionTreeClassifier(7)_max_featuresnull
sklearn.tree._classes.DecisionTreeClassifier(7)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeClassifier(7)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeClassifier(7)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeClassifier(7)_min_samples_split2
sklearn.tree._classes.DecisionTreeClassifier(7)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeClassifier(7)_monotonic_cstnull
sklearn.tree._classes.DecisionTreeClassifier(7)_random_state60115
sklearn.tree._classes.DecisionTreeClassifier(7)_splitter"best"

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

0.6704 ± 0.0579
Per class
0.7005 ± 0.0523
Per class
0.3409 ± 0.1155
0.3141 ± 0.1215
0.2995 ± 0.0529
0.4545 ± 0.0011
0.7005 ± 0.0529
768
Per class
0.7005 ± 0.0535
Per class
0.7005 ± 0.0529
0.9331 ± 0.0032
0.6589 ± 0.1166
0.4766 ± 0.0011
0.5472 ± 0.0491
1.1481 ± 0.1035
0.6704 ± 0.0579