61_random_patches_ensemble.py

# evaluate random patches ensemble algorithm for classification
from numpy import mean
from numpy import std
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.ensemble import BaggingClassifier

# define dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=5)

# define the model
model = BaggingClassifier(bootstrap=False, max_features=10, max_samples=0.5)

# evaluate the model
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)

n_scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1, error_score='raise')

# report performance
print('Accuracy: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))