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Task 3 (Supervised Classification) kr-vs-kp Uploaded 27-03-2017 by Randal Olson
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Flow

sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,randomforestcla ssifier=sklearn.ensemble.forest.RandomForestClassifier)(1)	Automatically created scikit-learn flow.
sklearn.ensemble.forest.RandomForestClassifier(16)_bootstrap	True
sklearn.ensemble.forest.RandomForestClassifier(16)_class_weight	None
sklearn.ensemble.forest.RandomForestClassifier(16)_criterion	gini
sklearn.ensemble.forest.RandomForestClassifier(16)_max_depth	None
sklearn.ensemble.forest.RandomForestClassifier(16)_max_features	auto
sklearn.ensemble.forest.RandomForestClassifier(16)_max_leaf_nodes	None
sklearn.ensemble.forest.RandomForestClassifier(16)_min_impurity_split	1e-07
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_leaf	1
sklearn.ensemble.forest.RandomForestClassifier(16)_min_samples_split	2
sklearn.ensemble.forest.RandomForestClassifier(16)_min_weight_fraction_leaf	0.0
sklearn.ensemble.forest.RandomForestClassifier(16)_n_estimators	500
sklearn.ensemble.forest.RandomForestClassifier(16)_n_jobs	-1
sklearn.ensemble.forest.RandomForestClassifier(16)_oob_score	False
sklearn.ensemble.forest.RandomForestClassifier(16)_random_state	None
sklearn.ensemble.forest.RandomForestClassifier(16)_verbose	0
sklearn.ensemble.forest.RandomForestClassifier(16)_warm_start	False
sklearn.decomposition.pca.PCA(1)_copy	True
sklearn.decomposition.pca.PCA(1)_iterated_power	auto
sklearn.decomposition.pca.PCA(1)_n_components	None
sklearn.decomposition.pca.PCA(1)_random_state	None
sklearn.decomposition.pca.PCA(1)_svd_solver	auto
sklearn.decomposition.pca.PCA(1)_tol	0.0
sklearn.decomposition.pca.PCA(1)_whiten	False
sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_steps	[('pca', PCA(copy=True, iterated_power='auto', n_components=None, random_state=None, svd_solver='auto', tol=0.0, whiten=False)), ('randomforestclassifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=None, max_features='auto', max_leaf_nodes=None, min_impurity_split=1e-07, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=-1, oob_score=False, random_state=None, verbose=0, warm_start=False))]