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from sklearn.datasets import load_iris | |
from sklearn.model_selection import train_test_split | |
from sklearn.preprocessing import StandardScaler | |
from sklearn.decomposition import PCA | |
from sklearn.pipeline import Pipeline | |
from sklearn import tree | |
# Load and split the data | |
iris = load_iris() | |
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42) | |
# Construct pipeline | |
pipe = Pipeline([('scl', StandardScaler()), | |
('pca', PCA(n_components=2)), | |
('clf', tree.DecisionTreeClassifier(random_state=42))]) | |
# Fit the pipeline | |
pipe.fit(X_train, y_train) | |
# Pipeline test accuracy | |
print('Test accuracy: %.3f' % pipe.score(X_test, y_test)) | |
# Pipeline estimator params; estimator is stored as step 3 ([2]), second item ([1]) | |
print('\nModel hyperparameters:\n', pipe.steps[2][1].get_params()) |
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