Run 10594106

Task 3 (Supervised Classification) kr-vs-kp Uploaded 02-02-2024 by Jano P
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sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.One HotEncoder,functiontransformer=sklearn.preprocessing._function_transformer. FunctionTransformer,symbolicclassifier=HROCH.classifier.SymbolicClassifier( estimator=HROCH.classifier.NonlinearLogisticRegressor))(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`. For an example use case of `Pipeline` combined with :class:`~sklearn.model_selection.GridSearchCV`, refer to :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The example :ref:`sphx_glr_auto_exampl...
HROCH.classifier.NonlinearLogisticRegressor(1)_algo_settings	null
HROCH.classifier.NonlinearLogisticRegressor(1)_class_weight	null
HROCH.classifier.NonlinearLogisticRegressor(1)_code_settings	null
HROCH.classifier.NonlinearLogisticRegressor(1)_const_settings	null
HROCH.classifier.NonlinearLogisticRegressor(1)_cv_params	null
HROCH.classifier.NonlinearLogisticRegressor(1)_feature_probs	null
HROCH.classifier.NonlinearLogisticRegressor(1)_init_const_settings	null
HROCH.classifier.NonlinearLogisticRegressor(1)_iter_limit	0
HROCH.classifier.NonlinearLogisticRegressor(1)_metric	"LogLoss"
HROCH.classifier.NonlinearLogisticRegressor(1)_num_threads	1
HROCH.classifier.NonlinearLogisticRegressor(1)_population_settings	null
HROCH.classifier.NonlinearLogisticRegressor(1)_precision	"f32"
HROCH.classifier.NonlinearLogisticRegressor(1)_problem	"math"
HROCH.classifier.NonlinearLogisticRegressor(1)_random_state	0
HROCH.classifier.NonlinearLogisticRegressor(1)_target_clip	null
HROCH.classifier.NonlinearLogisticRegressor(1)_time_limit	5.0
HROCH.classifier.NonlinearLogisticRegressor(1)_transformation	"LOGISTIC"
HROCH.classifier.NonlinearLogisticRegressor(1)_verbose	0
HROCH.classifier.NonlinearLogisticRegressor(1)_warm_start	false
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer,symbolicclassifier=HROCH.classifier.SymbolicClassifier(estimator=HROCH.classifier.NonlinearLogisticRegressor))(1)_memory	null
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer,symbolicclassifier=HROCH.classifier.SymbolicClassifier(estimator=HROCH.classifier.NonlinearLogisticRegressor))(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "functiontransformer", "step_name": "functiontransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "symbolicclassifier", "step_name": "symbolicclassifier"}}]
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer,symbolicclassifier=HROCH.classifier.SymbolicClassifier(estimator=HROCH.classifier.NonlinearLogisticRegressor))(1)_verbose	false
sklearn.preprocessing._encoders.OneHotEncoder(50)_categories	"auto"
sklearn.preprocessing._encoders.OneHotEncoder(50)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(50)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(50)_feature_name_combiner	"concat"
sklearn.preprocessing._encoders.OneHotEncoder(50)_handle_unknown	"infrequent_if_exist"
sklearn.preprocessing._encoders.OneHotEncoder(50)_max_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(50)_min_frequency	null
sklearn.preprocessing._encoders.OneHotEncoder(50)_sparse	"deprecated"
sklearn.preprocessing._encoders.OneHotEncoder(50)_sparse_output	true
sklearn.preprocessing._function_transformer.FunctionTransformer(6)_accept_sparse	true
sklearn.preprocessing._function_transformer.FunctionTransformer(6)_check_inverse	true
sklearn.preprocessing._function_transformer.FunctionTransformer(6)_feature_names_out	null
sklearn.preprocessing._function_transformer.FunctionTransformer(6)_func	{"oml-python:serialized_object": "function", "value": "__main__."}
sklearn.preprocessing._function_transformer.FunctionTransformer(6)_inv_kw_args	null
sklearn.preprocessing._function_transformer.FunctionTransformer(6)_inverse_func	null
sklearn.preprocessing._function_transformer.FunctionTransformer(6)_kw_args	null
sklearn.preprocessing._function_transformer.FunctionTransformer(6)_validate	false