import numpy as np
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.ensemble import AdaBoostClassifier

data = load_digits()
X = data.data
y = data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
model = AdaBoostClassifier(base_estimator=None, n_estimators=20, learning_rate=1,
                           algorithm='SAMME', random_state=42).fit(X_train, y_train)


print(np.mean(np.argmax(model.predict_proba(X_test), axis=1) == model.predict(X_test)))

print(model.predict_proba(X_test)[0]) gives the class probability scores for the first test example:
[0.09949952 0.1000983  0.10002473 0.09970723 0.10019514 0.10022755
 **0.10087143** 0.09974335 0.10005309 0.09957965]

print(model.predict(X_test)[0])
Result: 5