How to evaluate XGBoost model with learning curves

def Snippet_188(): 
    print()
    print(format('Hoe to evaluate XGBoost model with learning curves','*^82'))    
    
    import warnings
    warnings.filterwarnings("ignore")
    
    # load libraries
    import numpy as np
    from xgboost import XGBClassifier
    import matplotlib.pyplot as plt    
    
    plt.style.use('ggplot')
    
    from sklearn import datasets
    import matplotlib.pyplot as plt
    from sklearn.model_selection import learning_curve
    
    # load the datasets
    dataset = datasets.load_breast_cancer()
    X = dataset.data; y = dataset.target

    # Create CV training and test scores for various training set sizes
    train_sizes, train_scores, test_scores = learning_curve(XGBClassifier(), 
                                               X, y, cv=10, scoring='accuracy', n_jobs=-1, 
                                               # 50 different sizes of the training set
                                               train_sizes=np.linspace(0.01, 1.0, 50))
    
    # Create means and standard deviations of training set scores
    train_mean = np.mean(train_scores, axis=1)
    train_std = np.std(train_scores, axis=1)
    
    # Create means and standard deviations of test set scores
    test_mean = np.mean(test_scores, axis=1)
    test_std = np.std(test_scores, axis=1)
    
    # Draw lines
    plt.subplots(figsize=(12,12))
    plt.plot(train_sizes, train_mean, '--', color="#111111",  label="Training score")
    plt.plot(train_sizes, test_mean, color="#111111", label="Cross-validation score")

    # Draw bands
    plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color="#DDDDDD")
    plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, color="#DDDDDD")
    
    # Create plot
    plt.title("Learning Curve")
    plt.xlabel("Training Set Size"), plt.ylabel("Accuracy Score"), plt.legend(loc="best")
    plt.tight_layout(); plt.show()
Snippet_188()

****************Hoe to evaluate XGBoost model with learning curves****************