Run
10592339

Run 10592339

Task 189908 (Supervised Classification) Fashion-MNIST Uploaded 22-03-2023 by Yinuo Guo
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.ensemble._forest.RandomForestClassifier)(2)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(42)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(42)_copytrue
sklearn.impute._base.SimpleImputer(42)_fill_valuenull
sklearn.impute._base.SimpleImputer(42)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(42)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(42)_strategy"mean"
sklearn.impute._base.SimpleImputer(42)_verbose"deprecated"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._forest.RandomForestClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._forest.RandomForestClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._forest.RandomForestClassifier)(2)_verbosefalse
sklearn.ensemble._forest.RandomForestClassifier(27)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(27)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(27)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(27)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(27)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(27)_max_features"sqrt"
sklearn.ensemble._forest.RandomForestClassifier(27)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(27)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(27)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(27)_min_samples_leaf1
sklearn.ensemble._forest.RandomForestClassifier(27)_min_samples_split2
sklearn.ensemble._forest.RandomForestClassifier(27)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(27)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(27)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(27)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(27)_random_state53760
sklearn.ensemble._forest.RandomForestClassifier(27)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(27)_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
0.991
Per class
Cross-validation details (33% Holdout set)
F measure
0.8842
Per class
Cross-validation details (33% Holdout set)
Kappa
0.8733
Cross-validation details (33% Holdout set)
KB relative information score
0.8442
Cross-validation details (33% Holdout set)
Mean absolute error
0.0471
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.18
Cross-validation details (33% Holdout set)
Weighted recall
0.886
Cross-validation details (33% Holdout set)
Number of instances
23100
Per class
Cross-validation details (33% Holdout set)
Precision
0.8849
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.886
Cross-validation details (33% Holdout set)
Prior entropy
3.3219
Cross-validation details (33% Holdout set)
Relative absolute error
0.2615
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.3
Cross-validation details (33% Holdout set)
Root mean squared error
0.1356
Cross-validation details (33% Holdout set)
Root relative squared error
0.4522
Cross-validation details (33% Holdout set)
Unweighted recall
0.8838
Cross-validation details (33% Holdout set)