Run
10559873

Run 10559873

Task 28 (Supervised Classification) optdigits Uploaded 26-03-2021 by louisot milijaona
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Flow

sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.Conditiona lImputer,hotencoding=sklearn.compose._column_transformer.ColumnTransformer( enc=sklearn.preprocessing._encoders.OneHotEncoder),variencethreshold=sklear n.feature_selection._variance_threshold.VarianceThreshold,classifier=sklear n.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``.
openmlstudy14.preprocessing.ConditionalImputer(9)_axis0
openmlstudy14.preprocessing.ConditionalImputer(9)_categorical_features[]
openmlstudy14.preprocessing.ConditionalImputer(9)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(9)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(9)_missing_valuesNaN
openmlstudy14.preprocessing.ConditionalImputer(9)_strategy"median"
openmlstudy14.preprocessing.ConditionalImputer(9)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(9)_verbose0
sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder)(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder)(1)_remainder"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "enc", "step_name": "enc", "argument_1": []}}]
sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
sklearn.preprocessing._encoders.OneHotEncoder(25)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(25)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(25)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(25)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(25)_sparsefalse
sklearn.feature_selection._variance_threshold.VarianceThreshold(3)_threshold0.0
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder),variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,classifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder),variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,classifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputation", "step_name": "imputation"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "hotencoding", "step_name": "hotencoding"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variencethreshold", "step_name": "variencethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder),variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,classifier=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbosefalse
sklearn.ensemble._forest.RandomForestClassifier(8)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(8)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestClassifier(8)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(8)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(8)_max_depthnull
sklearn.ensemble._forest.RandomForestClassifier(8)_max_features0.4696787193657851
sklearn.ensemble._forest.RandomForestClassifier(8)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestClassifier(8)_max_samplesnull
sklearn.ensemble._forest.RandomForestClassifier(8)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestClassifier(8)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(8)_min_samples_leaf11
sklearn.ensemble._forest.RandomForestClassifier(8)_min_samples_split17
sklearn.ensemble._forest.RandomForestClassifier(8)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestClassifier(8)_n_estimators100
sklearn.ensemble._forest.RandomForestClassifier(8)_n_jobsnull
sklearn.ensemble._forest.RandomForestClassifier(8)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(8)_random_state27600
sklearn.ensemble._forest.RandomForestClassifier(8)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(8)_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
0.9988 ± 0.0005
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9615 ± 0.0096
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9573 ± 0.0106
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.881 ± 0.0061
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0395 ± 0.0015
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.18 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9616 ± 0.0096
Cross-validation details (10-fold Crossvalidation)
Number of instances
5620
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9616 ± 0.0094
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9616 ± 0.0096
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.3218 ± 0
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2194 ± 0.0081
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1036 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3454 ± 0.0124
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9615 ± 0.0097
Cross-validation details (10-fold Crossvalidation)