Run
10435699

Run 10435699

Task 31 (Supervised Classification) credit-g Uploaded 10-01-2020 by Yaron Geffen
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(tranformer=sklearn.compose._column_transformer.Co lumnTransformer(one_hot_encoder=sklearn.preprocessing._encoders.OneHotEncod er),scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.li near_model._logistic.LogisticRegression)(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.preprocessing._data.StandardScaler(1)_copytrue
sklearn.preprocessing._data.StandardScaler(1)_with_meanfalse
sklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
sklearn.preprocessing._encoders.OneHotEncoder(19)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(19)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(19)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(19)_handle_unknown"error"
sklearn.preprocessing._encoders.OneHotEncoder(19)_sparsetrue
sklearn.compose._column_transformer.ColumnTransformer(one_hot_encoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(one_hot_encoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_remainder"drop"
sklearn.compose._column_transformer.ColumnTransformer(one_hot_encoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(one_hot_encoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformer_weightsnull
sklearn.compose._column_transformer.ColumnTransformer(one_hot_encoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_transformers[{"oml-python:serialized_object": "component_reference", "value": {"key": "one_hot_encoder", "step_name": "one_hot_encoder", "argument_1": [true, false, true, true, false, true, true, false, true, true, false, true, false, true, true, false, true, false, true, true]}}]
sklearn.compose._column_transformer.ColumnTransformer(one_hot_encoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
sklearn.pipeline.Pipeline(tranformer=sklearn.compose._column_transformer.ColumnTransformer(one_hot_encoder=sklearn.preprocessing._encoders.OneHotEncoder),scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.linear_model._logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.Pipeline(tranformer=sklearn.compose._column_transformer.ColumnTransformer(one_hot_encoder=sklearn.preprocessing._encoders.OneHotEncoder),scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.linear_model._logistic.LogisticRegression)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "tranformer", "step_name": "tranformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
sklearn.pipeline.Pipeline(tranformer=sklearn.compose._column_transformer.ColumnTransformer(one_hot_encoder=sklearn.preprocessing._encoders.OneHotEncoder),scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.linear_model._logistic.LogisticRegression)(1)_verbosefalse
sklearn.linear_model._logistic.LogisticRegression(1)_C0.1
sklearn.linear_model._logistic.LogisticRegression(1)_class_weightnull
sklearn.linear_model._logistic.LogisticRegression(1)_dualfalse
sklearn.linear_model._logistic.LogisticRegression(1)_fit_intercepttrue
sklearn.linear_model._logistic.LogisticRegression(1)_intercept_scaling1
sklearn.linear_model._logistic.LogisticRegression(1)_l1_rationull
sklearn.linear_model._logistic.LogisticRegression(1)_max_iter100
sklearn.linear_model._logistic.LogisticRegression(1)_multi_class"auto"
sklearn.linear_model._logistic.LogisticRegression(1)_n_jobsnull
sklearn.linear_model._logistic.LogisticRegression(1)_penalty"l2"
sklearn.linear_model._logistic.LogisticRegression(1)_random_state13067
sklearn.linear_model._logistic.LogisticRegression(1)_solver"lbfgs"
sklearn.linear_model._logistic.LogisticRegression(1)_tol0.0001
sklearn.linear_model._logistic.LogisticRegression(1)_verbose0
sklearn.linear_model._logistic.LogisticRegression(1)_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

0.764 ± 0.0517
Per class
Cross-validation details (10-fold Crossvalidation)
0.7338 ± 0.0349
Per class
Cross-validation details (10-fold Crossvalidation)
0.3451 ± 0.087
Cross-validation details (10-fold Crossvalidation)
0.2014 ± 0.0491
Cross-validation details (10-fold Crossvalidation)
0.3307 ± 0.0158
Cross-validation details (10-fold Crossvalidation)
0.4202
Cross-validation details (10-fold Crossvalidation)
0.748 ± 0.0326
Cross-validation details (10-fold Crossvalidation)
1000
Per class
Cross-validation details (10-fold Crossvalidation)
0.7329 ± 0.0367
Per class
Cross-validation details (10-fold Crossvalidation)
0.748 ± 0.0326
Cross-validation details (10-fold Crossvalidation)
0.8813
Cross-validation details (10-fold Crossvalidation)
0.7871 ± 0.0376
Cross-validation details (10-fold Crossvalidation)
0.4583
Cross-validation details (10-fold Crossvalidation)
0.4144 ± 0.0225
Cross-validation details (10-fold Crossvalidation)
0.9042 ± 0.049
Cross-validation details (10-fold Crossvalidation)
0.6581 ± 0.0434
Cross-validation details (10-fold Crossvalidation)