import numpy as np
from sklearn.datasets import make_classification
from sklearn.ensemble import AdaBoostClassifier
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

X, y = make_classification(n_features=100, n_samples=1000, random_state=42, n_classes=2, n_informative=7)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)

model = AdaBoostClassifier(n_estimators=2, algorithm="SAMME", random_state=42,
                                                base_estimator=DecisionTreeClassifier(
                                                    max_depth=5, random_state=42))
model.fit(X_train, y_train)
print(np.mean(model.predict(X_test) == np.argmax(model.predict_proba(X_test), axis=1)))