Run 10593280

Task 32 (Supervised Classification) pendigits Uploaded 01-04-2023 by Ribeiro Pedro
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sklearn.pipeline.Pipeline(scale=sklearn.preprocessing._data.MinMaxScaler,es timator=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(35)_ccp_alpha	0.0
sklearn.tree._classes.DecisionTreeClassifier(35)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(35)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(35)_max_depth	null
sklearn.tree._classes.DecisionTreeClassifier(35)_max_features	null
sklearn.tree._classes.DecisionTreeClassifier(35)_max_leaf_nodes	null
sklearn.tree._classes.DecisionTreeClassifier(35)_min_impurity_decrease	0.0
sklearn.tree._classes.DecisionTreeClassifier(35)_min_samples_leaf	1
sklearn.tree._classes.DecisionTreeClassifier(35)_min_samples_split	2
sklearn.tree._classes.DecisionTreeClassifier(35)_min_weight_fraction_leaf	0.0
sklearn.tree._classes.DecisionTreeClassifier(35)_random_state	22450
sklearn.tree._classes.DecisionTreeClassifier(35)_splitter	"best"
sklearn.pipeline.Pipeline(scale=sklearn.preprocessing._data.MinMaxScaler,estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(scale=sklearn.preprocessing._data.MinMaxScaler,estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "scale", "step_name": "scale"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(scale=sklearn.preprocessing._data.MinMaxScaler,estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false
sklearn.preprocessing._data.MinMaxScaler(4)_clip	false
sklearn.preprocessing._data.MinMaxScaler(4)_copy	true
sklearn.preprocessing._data.MinMaxScaler(4)_feature_range	[0, 1]