XGBoost - does it make sense that accuracy decreases as threshold increases? I'm using XGBoost for a classification problem, and if I need to check how accuracy changes as a function of threshold. As a result, I got that accuracy decreases as the threshold value increases (see plot below). Does that make sense?

Here is my code:
num_col = df.shape[1]

# split data into X and y
X = df.iloc[:,2:(num_col-1)]
y = df.iloc[:,num_col-1]

# split data into train and test sets
seed = 7
test_size = 0.33

# With the stratified split, we take into account class imbalances. 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101, stratify=y)

model = XGBClassifier()
model.fit(X_train, y_train)

threshold = []
accuracy = []

for p in tqdm([0.5, 0.6, 0.7, 0.8, 0.9, 0.95]):
    threshold.append(p)
    y_pred = (model.predict_proba(X_test)[:,1] >= p).astype(int)
    predictions = [round(value) for value in y_pred]
    accuracy.append(accuracy_score(y_test,predictions))

plt.scatter(threshold,accuracy)
plt.xlabel("Threshold")
plt.ylabel("Balanced accuracy")
plt.show()


This makes sense, as you increase your threshold and apply an arbitrary cutoff to the predicted probabilities you will increasingly classify all units to the majority class (which represents 98.45 % of your data), which is what you see in your plot, the accuracy drops towards 0.9845. If you try a threshold of say 0.999 you should get exactly 0.9845.
