Run
10457903

Run 10457903

Task 9900 (Supervised Classification) abalone Uploaded 19-05-2020 by Marc Zöller
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • automl_meta_features openml-python Sklearn_0.22.1.
Add a single new tag. Use underscores for spaces. Press enter when done.
Add tag
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.on e_hot_encoding.OneHotEncoderComponent,step_1=sklearn.impute._knn.KNNImputer ,step_2=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.ensemble._forest.RandomForestClassifier(2)_bootstrapfalse
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.618896783934749
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"entropy"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth9
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features5
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes1637
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.2799958763442346
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.6236289886702749
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.11469771021941025
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.029348399848819817
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.06987652781663994
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators767
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs1
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scorefalse
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state42
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_warm_startfalse
sklearn.impute._knn.KNNImputer(1)_add_indicatortrue
sklearn.impute._knn.KNNImputer(1)_copyfalse
sklearn.impute._knn.KNNImputer(1)_metric"nan_euclidean"
sklearn.impute._knn.KNNImputer(1)_missing_valuesNaN
sklearn.impute._knn.KNNImputer(1)_n_neighbors23
sklearn.impute._knn.KNNImputer(1)_weights"uniform"
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.impute._knn.KNNImputer,step_2=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.impute._knn.KNNImputer,step_2=sklearn.ensemble._forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.impute._knn.KNNImputer,step_2=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.

16 Evaluation measures

0.499
Per class
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.5
0.0001 ± 0
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore000.0…0.0001-0.0…-0.0000250.0…0.0000250.0…0.000050.0…0.0000750.0…0.000125
0.4441 ± 0
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.4…0.44410.4…0.44420.4…0.4440750.4…0.4441250.4…0.444150.4…0.4441750.4…0.444225
0.4441 ± 0
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.4…0.44410.4…0.44420.4…0.4440750.4…0.4441250.4…0.444150.4…0.4441750.4…0.444225
0.3464 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.3450.34550.3460.34650.347
4177
Per class
Cross-validation details (10-fold Crossvalidation)
0.3464 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.3450.34550.3460.34650.347
1.584 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore01.583851.58391.583951.5841.584051.58411.584151.58421.58425
1 ± 0
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore01
0.4712 ± 0
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.4…0.47120.4…0.47130.4…0.4711750.4…0.4712250.4…0.471250.4…0.4712750.4…0.471325
0.4712 ± 0
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.4…0.47120.4…0.47130.4…0.4711750.4…0.4712250.4…0.471250.4…0.4712750.4…0.471325
1 ± 0
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore01
0.3333
Cross-validation details (10-fold Crossvalidation)
Created with Highcharts 5.0.7RepeatScore00.3333
­