Run 10587703

Task 3 (Supervised Classification) kr-vs-kp Uploaded 10-02-2022 by Quan Deng
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

Issue	#Downvotes for this reason	By

Flow

sklearn.pipeline.Pipeline(encoder=sklearn.preprocessing._encoders.OrdinalEn coder,clf=sklearn.tree._classes.DecisionTreeClassifier)(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.tree._classes.DecisionTreeClassifier(23)_ccp_alpha	0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(23)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(23)_max_depth	null
sklearn.tree._classes.DecisionTreeClassifier(23)_max_features	null
sklearn.tree._classes.DecisionTreeClassifier(23)_max_leaf_nodes	null
sklearn.tree._classes.DecisionTreeClassifier(23)_min_impurity_decrease	0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_min_samples_leaf	1
sklearn.tree._classes.DecisionTreeClassifier(23)_min_samples_split	2
sklearn.tree._classes.DecisionTreeClassifier(23)_min_weight_fraction_leaf	0.0
sklearn.tree._classes.DecisionTreeClassifier(23)_random_state	43003
sklearn.tree._classes.DecisionTreeClassifier(23)_splitter	"best"
sklearn.pipeline.Pipeline(encoder=sklearn.preprocessing._encoders.OrdinalEncoder,clf=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(encoder=sklearn.preprocessing._encoders.OrdinalEncoder,clf=sklearn.tree._classes.DecisionTreeClassifier)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "encoder", "step_name": "encoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "clf", "step_name": "clf"}}]
sklearn.pipeline.Pipeline(encoder=sklearn.preprocessing._encoders.OrdinalEncoder,clf=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false
sklearn.preprocessing._encoders.OrdinalEncoder(4)_categories	"auto"
sklearn.preprocessing._encoders.OrdinalEncoder(4)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OrdinalEncoder(4)_handle_unknown	"use_encoded_value"
sklearn.preprocessing._encoders.OrdinalEncoder(4)_unknown_value	-1