57_bagging_ensemble_classification.py

# evaluate bagging 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()

# 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)))


# make predictions using bagging for classification
from sklearn.datasets import make_classification
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()

# fit the model on the whole dataset
model.fit(X, y)

# make a single prediction
row = [[-4.7705504,-1.88685058,-0.96057964,2.53850317,-6.5843005,3.45711663,-7.46225013,2.01338213,-0.45086384,-1.89314931,-2.90675203,-0.21214568,-0.9623956,3.93862591,0.06276375,0.33964269,4.0835676,1.31423977,-2.17983117,3.1047287]]

yhat = model.predict(row)
print('Predicted Class: %d' % yhat[0])