Suppose you are analysing the performance of a new email spam detection system using precision and recall. You have already computed these metrics, and you are about to explore their trade-offs to optimise the classifier's threshold. Given the code snippet below, identify the correct function call that would allow you to adjust and visualise the precision-recall trade-off.from sklearn.metrics import precision_recall_curveimport matplotlib.pyplot as pltfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.model_selection import train_test_splitfrom sklearn.datasets import make_classification# Generate synthetic data for binary classificationX, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)# Split data into training and testing setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# Train a RandomForest classifierclassifier = RandomForestClassifier(random_state=42)classifier.fit(X_train, y_train)# Predict probabilities for the test sety_scores = classifier.predict_proba(X_test)[:, 1]# [Your Code Here] - Generate precision and recall values for various thresholdsplt.plot(precision_recall_curve(y_test, y_scores))precision, recall, thresholds = precision_recall_curve(y_test, y_scores)precision_recall_curve(classifier, X_test, y_test)precision, recall = precision_recall_curve(y_test, y_scores)

precision, recall, thresholds = precision_recall_curve(y_test, y_scores)
plt.plot(recall, precision)
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision-Recall curve')
plt.show()