for r, _ in enumerate(grid.cv_results_['mean_test_score']):
print(
"%.3f+/-%.2f %r" %
(grid.cv_results_['mean_test_score'][r],
grid.cv_results_['std_test_score'][r], grid.cv_results_['params'][r]))
print('Best parameters: %s' % grid.best_params_)
print('Accuracy: %.2f' % grid.best_score_)
from sklearn.ensemble import StackingClassifier
estimators = [('dt', clf2), ('kn', pip3)]
clf4 = StackingClassifier(estimators=estimators, final_estimator=pip1)
clf4.fit(X_train, y_train).score(X_test, y_test)
clf4.get_params()
# bagging
import pandas as pd
df_wine = pd.read_csv(
'https://archive.ics.uci.edu/ml/'
'machine-learning-databases/wine/wine.data',
header=None)
df_wine.columns = [
'Class label', 'Alcohol', 'Malic Acid', 'Ash', 'Alcalinity of ash',
'Magnesium', 'Total Phenoles', 'Flavanoids', 'Nonflaveoid phenold',
'Prantocyanins', 'color intensity', 'Hue', 'OD280/OD315 of diluted wines',
'Proline'
]
#drop 1 class
estimators = [
('naive-bayes', GaussianNB()),
('random-forest', rfc(n_estimators = 100, random_state = 0)),
('mlp', MLPClassifier(activation = "relu", alpha = 0.05, random_state = 0))
]
# Setting up the Meta-Classifier
clf = StackingClassifier(
estimators = estimators,
final_estimator = LogisticRegression(random_state = 0)
)
# fitting my model
clf.fit(x_train, y_train)
# getting info about the hyperparameters
clf.get_params()
'''
{'cv': None,
'estimators': [('naive-bayes', GaussianNB(priors=None, var_smoothing=1e-09)),
('random-forest',
RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_jobs=None, oob_score=False, random_state=0, verbose=0,
warm_start=False)),
('mlp',
MLPClassifier(activation='relu', alpha=0.05, batch_size='auto', beta_1=0.9,
