def predict_result(ybl, random_state):
    print(ybl)
    X_train, X_test, y_train, y_test = train_test_split(df[list_W], (df[str(ybl)] * 10).astype('int'), test_size=0.30, random_state = random_state)
    X_train2, X_test2 = train_test_split(df, test_size=0.30, random_state = random_state)
    classifier = GradientBoostingClassifier()
    classifier.fit(X_train, y_train)
    #print("X_train:", X_train)
    #print("y_train:", y_train)
    y_pred = classifier.predict(X_test)
    #print("X_test:", X_test)
    #print("y_pred:", y_pred)
    result = confusion_matrix(y_test, y_pred)
    #print("Confusion Matrix:")
    #print(result)
    result1 = classification_report(y_test, y_pred)
    #print("Classification Report:", )
    #print(result1)
    result2 = accuracy_score(y_test, y_pred)
    print("Accuracy:", result2)
    predict = classifier.predict(test[list_W]) / 10
    print("Score得分为：", math.sqrt(mean_absolute_error(y_test / 10, y_pred / 10)))
    return predict
这是什么意思，能不能一行一行帮我解读一下