Run
9046713

Run 9046713

Task 125966 (Supervised Classification) SpeedDating Uploaded 10-04-2018 by Hilde Weerts
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • openml-python Sklearn_0.19.1. study_98
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.Condition alImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethre shold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sk learn.ensemble.forest.RandomForestClassifier)(1)Automatically created scikit-learn flow.
sklearn.preprocessing.data.OneHotEncoder(17)_categorical_features[0, 2, 6, 7, 8, 9, 12, 13, 14, 21, 22, 23, 24, 25, 26, 33, 34, 35, 36, 37, 38, 45, 46, 47, 48, 49, 50, 56, 57, 58, 59, 60, 67, 68, 69, 70, 71, 72, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 108, 112, 113, 114, 117, 118]
sklearn.preprocessing.data.OneHotEncoder(17)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(17)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(17)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(17)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(11)_threshold0.0
sklearn.ensemble.forest.RandomForestClassifier(32)_bootstrapfalse
sklearn.ensemble.forest.RandomForestClassifier(32)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(32)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(32)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(32)_max_features0.2619510015219124
sklearn.ensemble.forest.RandomForestClassifier(32)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(32)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(32)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(32)_min_samples_leaf16
sklearn.ensemble.forest.RandomForestClassifier(32)_min_samples_split8
sklearn.ensemble.forest.RandomForestClassifier(32)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(32)_n_estimators500
sklearn.ensemble.forest.RandomForestClassifier(32)_n_jobs1
sklearn.ensemble.forest.RandomForestClassifier(32)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(32)_random_state1
sklearn.ensemble.forest.RandomForestClassifier(32)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(32)_warm_startfalse
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.ensemble.forest.RandomForestClassifier)(1)_memorynull
hyperimp.utils.preprocessing.ConditionalImputer(1)_axis0
hyperimp.utils.preprocessing.ConditionalImputer(1)_categorical_features[0, 2, 6, 7, 8, 9, 12, 13, 14, 21, 22, 23, 24, 25, 26, 33, 34, 35, 36, 37, 38, 45, 46, 47, 48, 49, 50, 56, 57, 58, 59, 60, 67, 68, 69, 70, 71, 72, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 108, 112, 113, 114, 117, 118]
hyperimp.utils.preprocessing.ConditionalImputer(1)_copytrue
hyperimp.utils.preprocessing.ConditionalImputer(1)_fill_empty0
hyperimp.utils.preprocessing.ConditionalImputer(1)_missing_values"NaN"
hyperimp.utils.preprocessing.ConditionalImputer(1)_strategy"mean"
hyperimp.utils.preprocessing.ConditionalImputer(1)_strategy_nominal"most_frequent"
hyperimp.utils.preprocessing.ConditionalImputer(1)_verbose0

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.

17 Evaluation measures

Area under ROC curve
0.8618 ± 0.0149
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8366 ± 0.0069
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3406 ± 0.0296
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1293.5541 ± 21.0917
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2055 ± 0.0034
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2752 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Number of instances
8378
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8456 ± 0.0086
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8623 ± 0.0051
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.6457
Cross-validation details (10-fold Crossvalidation)
Recall
0.8623 ± 0.0051
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7466 ± 0.0123
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3709 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3134 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8449 ± 0.0168
Cross-validation details (10-fold Crossvalidation)